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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method development for geometric functions pt 2: $\\beta$ parameters with geometric functions.\n",
    "\n",
    "- 09/04/20 debugged\n",
    "- 26/02/20 v1\n",
    "\n",
    "Aims:\n",
    "\n",
    "- Develop $\\beta_{L,M}$ formalism.\n",
    "- Develop corresponding numerical methods - see [pt 1 notebook](https://epsproc.readthedocs.io/en/dev/methods/geometric_method_dev_260220_tidy_160320.html).\n",
    "- Analyse geometric terms - see [pt 1 notebook](https://epsproc.readthedocs.io/en/dev/methods/geometric_method_dev_260220_tidy_160320.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\beta_{L,M}^{MF}$ rewrite\n",
    "\n",
    "The various terms defined above can be used to redefine the full MF observables, expressed as a set of $\\beta_{L,M}$ coefficients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original (full) form for the MF equation, as implemented in `ePSproc.mfblm()` (note this is defined for an expansion in non-conjugate harmonics, $Y_{LM}(\\theta_{\\hat{k}},\\phi_{\\hat{k}})$):\n",
    "\n",
    "\\begin{eqnarray}\n",
    "\\beta_{L,-M}^{\\mu_{i},\\mu_{f}} & = & \\sum_{l,m,\\mu}\\sum_{l',m',\\mu'}(-1)^{M}(-1)^{m}(-1)^{(\\mu'-\\mu_{0})}\\left(\\frac{(2l+1)(2l'+1)(2L+1)}{4\\pi}\\right)^{1/2}\\left(\\begin{array}{ccc}\n",
    "l & l' & L\\\\\n",
    "0 & 0 & 0\n",
    "\\end{array}\\right)\\left(\\begin{array}{ccc}\n",
    "l & l' & L\\\\\n",
    "-m & m' & -M\n",
    "\\end{array}\\right)\\nonumber \\\\\n",
    " & \\times & \\sum_{P,R',R}(2P+1)(-1)^{(R'-R)}\\left(\\begin{array}{ccc}\n",
    "1 & 1 & P\\\\\n",
    "\\mu & -\\mu' & R'\n",
    "\\end{array}\\right)\\left(\\begin{array}{ccc}\n",
    "1 & 1 & P\\\\\n",
    "\\mu_{0} & -\\mu_{0} & R\n",
    "\\end{array}\\right)D_{-R',-R}^{P}(R_{\\hat{n}})I_{l,m,\\mu}^{p_{i}\\mu_{i},p_{f}\\mu_{f}}(E)I_{l',m',\\mu'}^{p_{i}\\mu_{i},p_{f}\\mu_{f}*}(E)\n",
    "\\end{eqnarray}\n",
    "\n",
    "Where $I_{l,m,\\mu}^{p_{i}\\mu_{i},p_{f}\\mu_{f}}(E)$ are the energy-dependent dipole matrix elements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Substituting the terms above (plus some tidying up with phases etc. - see other notes for full derivation):\n",
    "\n",
    "\\begin{eqnarray}\n",
    "\\beta_{L,-M}^{\\mu_{i},\\mu_{f}} & = & \\sum_{l,m,\\mu}\\sum_{l',m',\\mu'}(-1)^{(\\mu'-\\mu_{0})}{B_{L,-M}}\\nonumber \\\\\n",
    " & \\times & \\sum_{P,R',R}(2P+1)^{\\frac{1}{2}}{E_{P-R}(\\hat{e})\\Lambda_{R',R}(R_{\\hat{n}})}I_{l,m,\\mu}^{p_{i}\\mu_{i},p_{f}\\mu_{f}}(E)I_{l',m',\\mu'}^{p_{i}\\mu_{i},p_{f}\\mu_{f}*}(E)\n",
    "\\end{eqnarray}\n",
    "\n",
    "And can also reorder...\n",
    "\n",
    "\\begin{eqnarray*}\n",
    "\\beta_{L,-M}^{\\mu_{i},\\mu_{f}} & = & \\sum_{P,R',R}(2P+1)^{\\frac{1}{2}}{E_{P-R}(\\hat{e})}\\sum_{l,m,\\mu}\\sum_{l',m',\\mu'}(-1)^{(\\mu'-\\mu_{0})}{B_{L,-M}\\Lambda_{R',R}(R_{\\hat{n}})}I_{l,m,\\mu}^{p_{i}\\mu_{i},p_{f}\\mu_{f}}(E)I_{l',m',\\mu'}^{p_{i}\\mu_{i},p_{f}\\mu_{f}*}(E)\n",
    "\\end{eqnarray*}\n",
    "\n",
    "Add additional (function) labels to track QNs:\n",
    "\n",
    "\\begin{eqnarray*}\n",
    "\\beta_{L,-M}^{\\mu_{i},\\mu_{f}} & = & \\sum_{P,R',R}(2P+1)^{\\frac{1}{2}}{E_{P-R}(\\hat{e};\\mu_{0})}\\sum_{l,m,\\mu}\\sum_{l',m',\\mu'}(-1)^{(\\mu'-\\mu_{0})}{\\Lambda_{R',R}(R_{\\hat{n}};\\mu,P,R,R')B_{L,-M}(l,l',m,m')}I_{l,m,\\mu}^{p_{i}\\mu_{i},p_{f}\\mu_{f}}(E)I_{l',m',\\mu'}^{p_{i}\\mu_{i},p_{f}\\mu_{f}*}(E)\n",
    "\\end{eqnarray*}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cf. R\\&U rotational wavepacket case, see also Chpt. 12 in Quantum\n",
    "Met book for similar formalism separating geometric channels:\n",
    "\n",
    "\\begin{equation}\n",
    "\\varUpsilon_{L,M}^{u}=\\sum_{ll'}\\sum_{\\lambda\\lambda'}\\sum_{mm'}(-1)^{m}\\sqrt{\\frac{(2l+1)(2l'+1)(2L+1)}{4\\pi}}\\left(\\begin{array}{ccc}\n",
    "l & l' & L\\\\\n",
    "m & -m' & M\n",
    "\\end{array}\\right)\\left(\\begin{array}{ccc}\n",
    "l & l' & L\\\\\n",
    "0 & 0 & 0\n",
    "\\end{array}\\right)\\gamma_{\\alpha_{+}l\\lambda ml'\\lambda'm'}^{u}\\label{eq:geom-channels}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\Lambda^{\\zeta\\zeta'}=\\sum_{\\Gamma,\\Gamma'}\\sum_{\\mu,\\mu'}\\sum_{h,h'}b_{hl\\lambda}^{\\Gamma\\mu*}b_{h'l'\\lambda'}^{\\Gamma'\\mu'}\\boldsymbol{D}_{hl}^{\\Gamma\\mu*}(q,\\,k)\\boldsymbol{D}_{h'l'}^{\\Gamma'\\mu'}(q',\\,k)\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\beta_{L,M}^{u}=\\sum_{\\zeta,\\zeta'}\\varUpsilon_{L,M}^{u,\\zeta\\zeta'}\\Lambda^{\\zeta\\zeta'}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For details on each term in the tensor product, see the [pt 1 notebook](https://epsproc.readthedocs.io/en/dev/methods/geometric_method_dev_260220_tidy_160320.html).  Here we'll look at the overall result, check for consistency and benchmark."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* pyevtk not found, VTK export not available. \n"
     ]
    }
   ],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "# Special functions\n",
    "# from scipy.special import sph_harm\n",
    "import spherical_functions as sf\n",
    "import quaternion\n",
    "\n",
    "# Performance & benchmarking libraries\n",
    "# from joblib import Memory\n",
    "# import xyzpy as xyz\n",
    "import numba as nb\n",
    "\n",
    "# Timings with ttictoc or time\n",
    "# https://github.com/hector-sab/ttictoc\n",
    "from ttictoc import TicToc\n",
    "import time\n",
    "\n",
    "# Package fns.\n",
    "# For module testing, include path to module here\n",
    "import sys\n",
    "import os\n",
    "modPath = r'D:\\code\\github\\ePSproc'  # Win test machine\n",
    "# modPath = r'/home/femtolab/github/ePSproc/'  # Linux test machine\n",
    "sys.path.append(modPath)\n",
    "import epsproc as ep\n",
    "# TODO: tidy this up!\n",
    "# from epsproc.util import matEleSelector\n",
    "# from epsproc.geomFunc import geomCalc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** ePSproc readMatEle(): scanning files for DumpIdy segments.\n",
      "\n",
      "*** Scanning file(s)\n",
      "['D:\\\\code\\\\github\\\\ePSproc\\\\data\\\\photoionization\\\\n2_3sg_0.1-50.1eV_A2.inp.out']\n",
      "\n",
      "*** Reading ePS output file:  D:\\code\\github\\ePSproc\\data\\photoionization\\n2_3sg_0.1-50.1eV_A2.inp.out\n",
      "Expecting 51 energy points.\n",
      "Expecting 2 symmetries.\n",
      "Scanning CrossSection segments.\n",
      "Expecting 102 DumpIdy segments.\n",
      "Found 102 dumpIdy segments (sets of matrix elements).\n",
      "\n",
      "Processing segments to Xarrays...\n",
      "Processed 102 sets of DumpIdy file segments, (0 blank)\n",
      "*** ePSproc readMatEle(): scanning files for CrossSection segments.\n",
      "\n",
      "*** Scanning file(s)\n",
      "['D:\\\\code\\\\github\\\\ePSproc\\\\data\\\\photoionization\\\\n2_3sg_0.1-50.1eV_A2.inp.out']\n",
      "\n",
      "*** Reading ePS output file:  D:\\code\\github\\ePSproc\\data\\photoionization\\n2_3sg_0.1-50.1eV_A2.inp.out\n",
      "Expecting 51 energy points.\n",
      "Expecting 2 symmetries.\n",
      "Scanning CrossSection segments.\n",
      "Expecting 3 CrossSection segments.\n",
      "Found 3 CrossSection segments (sets of results).\n",
      "Processed 3 sets of CrossSection file segments, (0 blank)\n"
     ]
    }
   ],
   "source": [
    "# Load data from modPath\\data\n",
    "dataPath = os.path.join(modPath, 'data', 'photoionization')\n",
    "dataFile = os.path.join(dataPath, 'n2_3sg_0.1-50.1eV_A2.inp.out')  # Set for sample N2 data for testing\n",
    "\n",
    "# Scan data file\n",
    "dataSet = ep.readMatEle(fileIn = dataFile)\n",
    "dataXS = ep.readMatEle(fileIn = dataFile, recordType = 'CrossSection')  # XS info currently not set in NO2 sample file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting data n2_3sg_0.1-50.1eV_A2.inp.out, pType=r, thres=0.01, with Seaborn\n"
     ]
    },
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       "      <td>0.032523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SU</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">SG</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">SU</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>6.246520</td>\n",
       "      <td>-10.081763</td>\n",
       "      <td>-8.912214</td>\n",
       "      <td>-5.342049</td>\n",
       "      <td>-3.160539</td>\n",
       "      <td>-1.795783</td>\n",
       "      <td>-0.867993</td>\n",
       "      <td>-0.174154</td>\n",
       "      <td>0.400643</td>\n",
       "      <td>0.926420</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.854886</td>\n",
       "      <td>-0.940017</td>\n",
       "      <td>-1.021577</td>\n",
       "      <td>-1.099776</td>\n",
       "      <td>-1.174787</td>\n",
       "      <td>-1.246759</td>\n",
       "      <td>-1.315814</td>\n",
       "      <td>-1.382058</td>\n",
       "      <td>-1.445582</td>\n",
       "      <td>-1.506467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>2.605768</td>\n",
       "      <td>2.775108</td>\n",
       "      <td>4.733473</td>\n",
       "      <td>1.677812</td>\n",
       "      <td>-1.450830</td>\n",
       "      <td>-4.187395</td>\n",
       "      <td>-6.554491</td>\n",
       "      <td>-8.601759</td>\n",
       "      <td>-10.343109</td>\n",
       "      <td>-11.747922</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.293053</td>\n",
       "      <td>-0.441416</td>\n",
       "      <td>-0.579578</td>\n",
       "      <td>-0.708075</td>\n",
       "      <td>-0.827415</td>\n",
       "      <td>-0.938075</td>\n",
       "      <td>-1.040506</td>\n",
       "      <td>-1.135134</td>\n",
       "      <td>-1.222361</td>\n",
       "      <td>-1.302566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.020864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024263</td>\n",
       "      <td>0.042694</td>\n",
       "      <td>0.059142</td>\n",
       "      <td>0.077308</td>\n",
       "      <td>0.099330</td>\n",
       "      <td>0.126227</td>\n",
       "      <td>0.157745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.490499</td>\n",
       "      <td>0.511546</td>\n",
       "      <td>0.531810</td>\n",
       "      <td>0.551281</td>\n",
       "      <td>0.569951</td>\n",
       "      <td>0.587815</td>\n",
       "      <td>0.604868</td>\n",
       "      <td>0.621107</td>\n",
       "      <td>0.636528</td>\n",
       "      <td>0.651132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039566</td>\n",
       "      <td>-0.041422</td>\n",
       "      <td>-0.043265</td>\n",
       "      <td>-0.045092</td>\n",
       "      <td>-0.046901</td>\n",
       "      <td>-0.048689</td>\n",
       "      <td>-0.050453</td>\n",
       "      <td>-0.052190</td>\n",
       "      <td>-0.053899</td>\n",
       "      <td>-0.055577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Eke                          0.1        1.1       2.1       3.1       4.1   \\\n",
       "Cont Targ Total l m  mu                                                      \n",
       "PU   SG   PU    1 -1  1 -6.203556   7.496908  3.926892  1.071093 -0.335132   \n",
       "                   1 -1 -6.203556   7.496908  3.926892  1.071093 -0.335132   \n",
       "                3 -1  1 -2.090641  -1.723467 -3.571018 -1.703191  0.232612   \n",
       "                   1 -1 -2.090641  -1.723467 -3.571018 -1.703191  0.232612   \n",
       "                5 -1  1  0.000000   0.013246  0.000000 -0.013096 -0.024367   \n",
       "                   1 -1  0.000000   0.013246  0.000000 -0.013096 -0.024367   \n",
       "                7 -1  1  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "                   1 -1  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "SU   SG   SU    1  0  0  6.246520 -10.081763 -8.912214 -5.342049 -3.160539   \n",
       "                3  0  0  2.605768   2.775108  4.733473  1.677812 -1.450830   \n",
       "                5  0  0  0.000000  -0.020864  0.000000  0.024263  0.042694   \n",
       "                7  0  0  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "Eke                          5.1       6.1       7.1        8.1        9.1   \\\n",
       "Cont Targ Total l m  mu                                                       \n",
       "PU   SG   PU    1 -1  1 -1.043391 -1.396885 -1.554984  -1.598812  -1.573327   \n",
       "                   1 -1 -1.043391 -1.396885 -1.554984  -1.598812  -1.573327   \n",
       "                3 -1  1  1.862713  3.201022  4.298839   5.198215   5.929303   \n",
       "                   1 -1  1.862713  3.201022  4.298839   5.198215   5.929303   \n",
       "                5 -1  1 -0.033455 -0.042762 -0.053765  -0.067354  -0.083946   \n",
       "                   1 -1 -0.033455 -0.042762 -0.053765  -0.067354  -0.083946   \n",
       "                7 -1  1  0.000000  0.000000  0.000000   0.000000   0.000000   \n",
       "                   1 -1  0.000000  0.000000  0.000000   0.000000   0.000000   \n",
       "SU   SG   SU    1  0  0 -1.795783 -0.867993 -0.174154   0.400643   0.926420   \n",
       "                3  0  0 -4.187395 -6.554491 -8.601759 -10.343109 -11.747922   \n",
       "                5  0  0  0.059142  0.077308  0.099330   0.126227   0.157745   \n",
       "                7  0  0  0.000000  0.000000  0.000000   0.000000   0.000000   \n",
       "\n",
       "Eke                      ...      41.1      42.1      43.1      44.1  \\\n",
       "Cont Targ Total l m  mu  ...                                           \n",
       "PU   SG   PU    1 -1  1  ... -0.396306 -0.422225 -0.448003 -0.473466   \n",
       "                   1 -1  ... -0.396306 -0.422225 -0.448003 -0.473466   \n",
       "                3 -1  1  ...  1.200463  1.040074  0.892501  0.757189   \n",
       "                   1 -1  ...  1.200463  1.040074  0.892501  0.757189   \n",
       "                5 -1  1  ... -0.325544 -0.318791 -0.312332 -0.306211   \n",
       "                   1 -1  ... -0.325544 -0.318791 -0.312332 -0.306211   \n",
       "                7 -1  1  ...  0.027469  0.028083  0.028678  0.029257   \n",
       "                   1 -1  ...  0.027469  0.028083  0.028678  0.029257   \n",
       "SU   SG   SU    1  0  0  ... -0.854886 -0.940017 -1.021577 -1.099776   \n",
       "                3  0  0  ... -0.293053 -0.441416 -0.579578 -0.708075   \n",
       "                5  0  0  ...  0.490499  0.511546  0.531810  0.551281   \n",
       "                7  0  0  ... -0.039566 -0.041422 -0.043265 -0.045092   \n",
       "\n",
       "Eke                          45.1      46.1      47.1      48.1      49.1  \\\n",
       "Cont Targ Total l m  mu                                                     \n",
       "PU   SG   PU    1 -1  1 -0.498463 -0.522867 -0.546573 -0.569493 -0.591558   \n",
       "                   1 -1 -0.498463 -0.522867 -0.546573 -0.569493 -0.591558   \n",
       "                3 -1  1  0.633556  0.521004  0.418928  0.326726  0.243800   \n",
       "                   1 -1  0.633556  0.521004  0.418928  0.326726  0.243800   \n",
       "                5 -1  1 -0.300456 -0.295091 -0.290130 -0.285584 -0.281453   \n",
       "                   1 -1 -0.300456 -0.295091 -0.290130 -0.285584 -0.281453   \n",
       "                7 -1  1  0.029823  0.030377  0.030923  0.031461  0.031994   \n",
       "                   1 -1  0.029823  0.030377  0.030923  0.031461  0.031994   \n",
       "SU   SG   SU    1  0  0 -1.174787 -1.246759 -1.315814 -1.382058 -1.445582   \n",
       "                3  0  0 -0.827415 -0.938075 -1.040506 -1.135134 -1.222361   \n",
       "                5  0  0  0.569951  0.587815  0.604868  0.621107  0.636528   \n",
       "                7  0  0 -0.046901 -0.048689 -0.050453 -0.052190 -0.053899   \n",
       "\n",
       "Eke                          50.1  \n",
       "Cont Targ Total l m  mu            \n",
       "PU   SG   PU    1 -1  1 -0.612712  \n",
       "                   1 -1 -0.612712  \n",
       "                3 -1  1  0.169566  \n",
       "                   1 -1  0.169566  \n",
       "                5 -1  1 -0.277737  \n",
       "                   1 -1 -0.277737  \n",
       "                7 -1  1  0.032523  \n",
       "                   1 -1  0.032523  \n",
       "SU   SG   SU    1  0  0 -1.506467  \n",
       "                3  0  0 -1.302566  \n",
       "                5  0  0  0.651132  \n",
       "                7  0  0 -0.055577  \n",
       "\n",
       "[12 rows x 51 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tabulate & plot matrix elements vs. Eke\n",
    "selDims = {'it':1, 'Type':'L'}\n",
    "matE = dataSet[0].sel(selDims)  # Set for N2 case, length-gauge results only.\n",
    "daPlot, daPlotpd, legendList, gFig = ep.lmPlot(matE, xDim = 'Eke', pType = 'r', fillna = True)\n",
    "daPlotpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerics\n",
    "\n",
    "The tensor product is currenty implemented in `mfblmXprod`, making use of direct multiplication of Xarrays corresponding to each term in the eqns.  (As of mid-March 2020, it still needs a bit of work, but is basically working.) See [pt 1 for more details of the component tensors](https://epsproc.readthedocs.io/en/dev/methods/geometric_method_dev_260220_090420_tidy.html).\n",
    "\n",
    "This is easy to follow, and relatively fast, although can be RAM heavy for large sets of matrix elements (despite the relatively sparse arrays). Parallelization is not yet implemented, but should be fairly easy using [Xarray's GroupBy functionality](http://xarray.pydata.org/en/stable/groupby.html). Fast functions are [used on the back-end](https://epsproc.readthedocs.io/en/dev/tests/Low_level_bench_tests_Wigner3j_Feb2020.html), although there is likely some significant speed-ups to be had by making use of GPU methods here (and in the tensor handling) - this will be necessary for larger problems, e.g. fitting data.\n",
    "\n",
    "Note - quite a bit of the processing is currently due to slow selection/thresholding routines, so there is also some improvements to be made there.\n",
    "\n",
    "Timing benchmarks are from an AMD Threadripper 1950X workstation (single core only)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Without symmetry summation\n",
    "Treat continua separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time = 5.240962028503418 seconds, for 51 energy points, 3 polarizations, threshold=0.0001.\n"
     ]
    }
   ],
   "source": [
    "phaseConvention = 'E'  # Set phase conventions used in the numerics - for ePolyScat matrix elements, set to 'E', to match defns. above.\n",
    "\n",
    "symSum = False  # Sum over symmetry groups, or keep separate?\n",
    "SFflag = True  # Include scaling factor to Mb in calculation?\n",
    "thres = 1e-4\n",
    "RX = ep.setPolGeoms()  # Set default pol geoms (z,x,y), or will be set by mfblmXprod() defaults\n",
    "\n",
    "start = time.time()\n",
    "mTermST, mTermS, mTermTest = ep.geomFunc.mfblmXprod(dataSet[0], QNs = None, thres = thres, selDims = {'it':1, 'Type':'L'}, thresDims='Eke', RX=RX, symSum=symSum, SFflag=True, phaseConvention=phaseConvention)\n",
    "end = time.time()\n",
    "print('Elapsed time = {0} seconds, for {1} energy points, {2} polarizations, threshold={3}.'.format((end-start), mTermST.Eke.size, RX.size, thres))\n",
    "\n",
    "# Elapsed time = 3.3885273933410645 seconds, for 51 energy points, 3 polarizations, threshold=0.01.\n",
    "# Elapsed time = 5.059587478637695 seconds, for 51 energy points, 3 polarizations, threshold=0.0001."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting data n2_3sg_0.1-50.1eV_A2.inp.out, pType=r, thres=0.01, with Seaborn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Full results (before summation)\n",
    "mTermST.attrs['dataType'] = 'matE'  # Set matE here to allow for correct plotting of sym dims.\n",
    "\n",
    "plotDimsRed = ['Labels','L','M']  # Set plotDims to fix dim ordering in plot\n",
    "if not symSum:\n",
    "    plotDimsRed.extend(['Cont','Targ','Total'])\n",
    "    \n",
    "daPlot, daPlotpd, legendList, gFig = ep.lmPlot(mTermST, plotDims=plotDimsRed, xDim='Eke', sumDims=None, pType = 'r', thres = 0.01, fillna = True, SFflag=False)  \n",
    "\n",
    "# daPlot, daPlotpd, legendList, gFig = ep.lmPlot(mTermST, xDim='Eke', sumDims=None, pType = 'r', thres = 0.01, fillna = True)  # If plotDims is not passed use default ordering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Eke</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1.1</th>\n",
       "      <th>2.1</th>\n",
       "      <th>3.1</th>\n",
       "      <th>4.1</th>\n",
       "      <th>5.1</th>\n",
       "      <th>6.1</th>\n",
       "      <th>7.1</th>\n",
       "      <th>8.1</th>\n",
       "      <th>9.1</th>\n",
       "      <th>...</th>\n",
       "      <th>41.1</th>\n",
       "      <th>42.1</th>\n",
       "      <th>43.1</th>\n",
       "      <th>44.1</th>\n",
       "      <th>45.1</th>\n",
       "      <th>46.1</th>\n",
       "      <th>47.1</th>\n",
       "      <th>48.1</th>\n",
       "      <th>49.1</th>\n",
       "      <th>50.1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cont</th>\n",
       "      <th>L</th>\n",
       "      <th>Labels</th>\n",
       "      <th>M</th>\n",
       "      <th>Targ</th>\n",
       "      <th>Total</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"38\" valign=\"top\">PU</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>x</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.272711-0.000000j</td>\n",
       "      <td>-0.638474-0.000000j</td>\n",
       "      <td>-0.775003-0.000000j</td>\n",
       "      <td>-0.814436-0.000000j</td>\n",
       "      <td>-0.794373+0.000000j</td>\n",
       "      <td>-0.736632+0.000000j</td>\n",
       "      <td>-0.655845-0.000000j</td>\n",
       "      <td>-0.562321-0.000000j</td>\n",
       "      <td>-0.463340-0.000000j</td>\n",
       "      <td>-0.363902-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.655141+0.000000j</td>\n",
       "      <td>0.650741+0.000000j</td>\n",
       "      <td>0.645402-0.000000j</td>\n",
       "      <td>0.639166-0.000000j</td>\n",
       "      <td>0.632075-0.000000j</td>\n",
       "      <td>0.624163+0.000000j</td>\n",
       "      <td>0.615465+0.000000j</td>\n",
       "      <td>0.606012+0.000000j</td>\n",
       "      <td>0.595832+0.000000j</td>\n",
       "      <td>0.584952+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.512102-0.000000j</td>\n",
       "      <td>0.586763+0.000000j</td>\n",
       "      <td>0.614632-0.000000j</td>\n",
       "      <td>0.622681-0.000000j</td>\n",
       "      <td>0.618586-0.000000j</td>\n",
       "      <td>0.606800-0.000000j</td>\n",
       "      <td>0.590309-0.000000j</td>\n",
       "      <td>0.571219+0.000000j</td>\n",
       "      <td>0.551014+0.000000j</td>\n",
       "      <td>0.530717-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322705+0.000000j</td>\n",
       "      <td>0.323603+0.000000j</td>\n",
       "      <td>0.324693-0.000000j</td>\n",
       "      <td>0.325966-0.000000j</td>\n",
       "      <td>0.327414+0.000000j</td>\n",
       "      <td>0.329029+0.000000j</td>\n",
       "      <td>0.330804+0.000000j</td>\n",
       "      <td>0.332734+0.000000j</td>\n",
       "      <td>0.334812+0.000000j</td>\n",
       "      <td>0.337033+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.512102-0.000000j</td>\n",
       "      <td>0.586763+0.000000j</td>\n",
       "      <td>0.614632-0.000000j</td>\n",
       "      <td>0.622681-0.000000j</td>\n",
       "      <td>0.618586-0.000000j</td>\n",
       "      <td>0.606800-0.000000j</td>\n",
       "      <td>0.590309-0.000000j</td>\n",
       "      <td>0.571219+0.000000j</td>\n",
       "      <td>0.551014+0.000000j</td>\n",
       "      <td>0.530717-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322705+0.000000j</td>\n",
       "      <td>0.323603+0.000000j</td>\n",
       "      <td>0.324693-0.000000j</td>\n",
       "      <td>0.325966-0.000000j</td>\n",
       "      <td>0.327414+0.000000j</td>\n",
       "      <td>0.329029+0.000000j</td>\n",
       "      <td>0.330804+0.000000j</td>\n",
       "      <td>0.332734+0.000000j</td>\n",
       "      <td>0.334812+0.000000j</td>\n",
       "      <td>0.337033+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.272711+0.000000j</td>\n",
       "      <td>-0.638474-0.000000j</td>\n",
       "      <td>-0.775003-0.000000j</td>\n",
       "      <td>-0.814436-0.000000j</td>\n",
       "      <td>-0.794373+0.000000j</td>\n",
       "      <td>-0.736632-0.000000j</td>\n",
       "      <td>-0.655845-0.000000j</td>\n",
       "      <td>-0.562321-0.000000j</td>\n",
       "      <td>-0.463340-0.000000j</td>\n",
       "      <td>-0.363902-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.655141+0.000000j</td>\n",
       "      <td>0.650741-0.000000j</td>\n",
       "      <td>0.645402+0.000000j</td>\n",
       "      <td>0.639166+0.000000j</td>\n",
       "      <td>0.632075+0.000000j</td>\n",
       "      <td>0.624163+0.000000j</td>\n",
       "      <td>0.615465+0.000000j</td>\n",
       "      <td>0.606012-0.000000j</td>\n",
       "      <td>0.595832-0.000000j</td>\n",
       "      <td>0.584952+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.512102+0.000000j</td>\n",
       "      <td>-0.586763+0.000000j</td>\n",
       "      <td>-0.614632+0.000000j</td>\n",
       "      <td>-0.622681+0.000000j</td>\n",
       "      <td>-0.618586+0.000000j</td>\n",
       "      <td>-0.606800+0.000000j</td>\n",
       "      <td>-0.590309+0.000000j</td>\n",
       "      <td>-0.571219+0.000000j</td>\n",
       "      <td>-0.551014+0.000000j</td>\n",
       "      <td>-0.530717+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.322705+0.000000j</td>\n",
       "      <td>-0.323603+0.000000j</td>\n",
       "      <td>-0.324693+0.000000j</td>\n",
       "      <td>-0.325966+0.000000j</td>\n",
       "      <td>-0.327414+0.000000j</td>\n",
       "      <td>-0.329029+0.000000j</td>\n",
       "      <td>-0.330804+0.000000j</td>\n",
       "      <td>-0.332734+0.000000j</td>\n",
       "      <td>-0.334812+0.000000j</td>\n",
       "      <td>-0.337033+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.512102-0.000000j</td>\n",
       "      <td>-0.586763-0.000000j</td>\n",
       "      <td>-0.614632-0.000000j</td>\n",
       "      <td>-0.622681-0.000000j</td>\n",
       "      <td>-0.618586-0.000000j</td>\n",
       "      <td>-0.606800-0.000000j</td>\n",
       "      <td>-0.590309-0.000000j</td>\n",
       "      <td>-0.571219-0.000000j</td>\n",
       "      <td>-0.551014-0.000000j</td>\n",
       "      <td>-0.530717-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.322705-0.000000j</td>\n",
       "      <td>-0.323603-0.000000j</td>\n",
       "      <td>-0.324693-0.000000j</td>\n",
       "      <td>-0.325966-0.000000j</td>\n",
       "      <td>-0.327414-0.000000j</td>\n",
       "      <td>-0.329029-0.000000j</td>\n",
       "      <td>-0.330804-0.000000j</td>\n",
       "      <td>-0.332734-0.000000j</td>\n",
       "      <td>-0.334812-0.000000j</td>\n",
       "      <td>-0.337033-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">4</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.059474-0.000000j</td>\n",
       "      <td>0.271217+0.000000j</td>\n",
       "      <td>0.432955-0.000000j</td>\n",
       "      <td>0.521218-0.000000j</td>\n",
       "      <td>0.562256-0.000000j</td>\n",
       "      <td>0.570921-0.000000j</td>\n",
       "      <td>0.557098-0.000000j</td>\n",
       "      <td>0.527855+0.000000j</td>\n",
       "      <td>0.488402+0.000000j</td>\n",
       "      <td>0.442583-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.522724+0.000000j</td>\n",
       "      <td>-0.532915+0.000000j</td>\n",
       "      <td>-0.542405+0.000000j</td>\n",
       "      <td>-0.551224+0.000000j</td>\n",
       "      <td>-0.559398+0.000000j</td>\n",
       "      <td>-0.566951-0.000000j</td>\n",
       "      <td>-0.573903-0.000000j</td>\n",
       "      <td>-0.580274-0.000000j</td>\n",
       "      <td>-0.586083+0.000000j</td>\n",
       "      <td>-0.591343+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.080503-0.000000j</td>\n",
       "      <td>-0.153387-0.000000j</td>\n",
       "      <td>-0.252803-0.000000j</td>\n",
       "      <td>-0.294656-0.000000j</td>\n",
       "      <td>-0.300540-0.000000j</td>\n",
       "      <td>-0.282866+0.000000j</td>\n",
       "      <td>-0.249932-0.000000j</td>\n",
       "      <td>-0.207619-0.000000j</td>\n",
       "      <td>-0.160148-0.000000j</td>\n",
       "      <td>-0.110502-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555041+0.000000j</td>\n",
       "      <td>0.556708+0.000000j</td>\n",
       "      <td>0.557821-0.000000j</td>\n",
       "      <td>0.558406+0.000000j</td>\n",
       "      <td>0.558483+0.000000j</td>\n",
       "      <td>0.558074+0.000000j</td>\n",
       "      <td>0.557198-0.000000j</td>\n",
       "      <td>0.555870+0.000000j</td>\n",
       "      <td>0.554108+0.000000j</td>\n",
       "      <td>0.551925+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.080503-0.000000j</td>\n",
       "      <td>-0.153387-0.000000j</td>\n",
       "      <td>-0.252803-0.000000j</td>\n",
       "      <td>-0.294656-0.000000j</td>\n",
       "      <td>-0.300540-0.000000j</td>\n",
       "      <td>-0.282866+0.000000j</td>\n",
       "      <td>-0.249932-0.000000j</td>\n",
       "      <td>-0.207619-0.000000j</td>\n",
       "      <td>-0.160148-0.000000j</td>\n",
       "      <td>-0.110502-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555041+0.000000j</td>\n",
       "      <td>0.556708+0.000000j</td>\n",
       "      <td>0.557821-0.000000j</td>\n",
       "      <td>0.558406+0.000000j</td>\n",
       "      <td>0.558483+0.000000j</td>\n",
       "      <td>0.558074+0.000000j</td>\n",
       "      <td>0.557198-0.000000j</td>\n",
       "      <td>0.555870+0.000000j</td>\n",
       "      <td>0.554108+0.000000j</td>\n",
       "      <td>0.551925+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.059474-0.000000j</td>\n",
       "      <td>0.271217+0.000000j</td>\n",
       "      <td>0.432955+0.000000j</td>\n",
       "      <td>0.521218-0.000000j</td>\n",
       "      <td>0.562256-0.000000j</td>\n",
       "      <td>0.570921-0.000000j</td>\n",
       "      <td>0.557098+0.000000j</td>\n",
       "      <td>0.527855-0.000000j</td>\n",
       "      <td>0.488402-0.000000j</td>\n",
       "      <td>0.442583-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.522724+0.000000j</td>\n",
       "      <td>-0.532915+0.000000j</td>\n",
       "      <td>-0.542405-0.000000j</td>\n",
       "      <td>-0.551224+0.000000j</td>\n",
       "      <td>-0.559398+0.000000j</td>\n",
       "      <td>-0.566951-0.000000j</td>\n",
       "      <td>-0.573903-0.000000j</td>\n",
       "      <td>-0.580274-0.000000j</td>\n",
       "      <td>-0.586083+0.000000j</td>\n",
       "      <td>-0.591343+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.080503+0.000000j</td>\n",
       "      <td>0.153387-0.000000j</td>\n",
       "      <td>0.252803-0.000000j</td>\n",
       "      <td>0.294656-0.000000j</td>\n",
       "      <td>0.300540-0.000000j</td>\n",
       "      <td>0.282866-0.000000j</td>\n",
       "      <td>0.249932-0.000000j</td>\n",
       "      <td>0.207619-0.000000j</td>\n",
       "      <td>0.160148-0.000000j</td>\n",
       "      <td>0.110502-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.555041+0.000000j</td>\n",
       "      <td>-0.556708+0.000000j</td>\n",
       "      <td>-0.557821+0.000000j</td>\n",
       "      <td>-0.558406+0.000000j</td>\n",
       "      <td>-0.558483+0.000000j</td>\n",
       "      <td>-0.558074+0.000000j</td>\n",
       "      <td>-0.557198+0.000000j</td>\n",
       "      <td>-0.555870+0.000000j</td>\n",
       "      <td>-0.554108+0.000000j</td>\n",
       "      <td>-0.551925+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.080503+0.000000j</td>\n",
       "      <td>0.153387+0.000000j</td>\n",
       "      <td>0.252803+0.000000j</td>\n",
       "      <td>0.294656+0.000000j</td>\n",
       "      <td>0.300540+0.000000j</td>\n",
       "      <td>0.282866+0.000000j</td>\n",
       "      <td>0.249932+0.000000j</td>\n",
       "      <td>0.207619+0.000000j</td>\n",
       "      <td>0.160148+0.000000j</td>\n",
       "      <td>0.110502+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.555041-0.000000j</td>\n",
       "      <td>-0.556708-0.000000j</td>\n",
       "      <td>-0.557821-0.000000j</td>\n",
       "      <td>-0.558406-0.000000j</td>\n",
       "      <td>-0.558483-0.000000j</td>\n",
       "      <td>-0.558074-0.000000j</td>\n",
       "      <td>-0.557198-0.000000j</td>\n",
       "      <td>-0.555870-0.000000j</td>\n",
       "      <td>-0.554108-0.000000j</td>\n",
       "      <td>-0.551925-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">6</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.058944+0.000000j</td>\n",
       "      <td>-0.107815-0.000000j</td>\n",
       "      <td>-0.158159+0.000000j</td>\n",
       "      <td>-0.207754-0.000000j</td>\n",
       "      <td>-0.255279-0.000000j</td>\n",
       "      <td>-0.299612+0.000000j</td>\n",
       "      <td>-0.339916-0.000000j</td>\n",
       "      <td>-0.375688-0.000000j</td>\n",
       "      <td>-0.406740-0.000000j</td>\n",
       "      <td>-0.433136-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.334833+0.000000j</td>\n",
       "      <td>-0.323955+0.000000j</td>\n",
       "      <td>-0.312985+0.000000j</td>\n",
       "      <td>-0.301929+0.000000j</td>\n",
       "      <td>-0.290790+0.000000j</td>\n",
       "      <td>-0.279573+0.000000j</td>\n",
       "      <td>-0.268281+0.000000j</td>\n",
       "      <td>-0.256919+0.000000j</td>\n",
       "      <td>-0.245491-0.000000j</td>\n",
       "      <td>-0.234000+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.040201-0.000000j</td>\n",
       "      <td>0.073410+0.000000j</td>\n",
       "      <td>0.107661+0.000000j</td>\n",
       "      <td>0.141347-0.000000j</td>\n",
       "      <td>0.173516-0.000000j</td>\n",
       "      <td>0.203384-0.000000j</td>\n",
       "      <td>0.230377-0.000000j</td>\n",
       "      <td>0.254147+0.000000j</td>\n",
       "      <td>0.274568+0.000000j</td>\n",
       "      <td>0.291696-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.201422+0.000000j</td>\n",
       "      <td>0.193886+0.000000j</td>\n",
       "      <td>0.186317-0.000000j</td>\n",
       "      <td>0.178717-0.000000j</td>\n",
       "      <td>0.171088+0.000000j</td>\n",
       "      <td>0.163434+0.000000j</td>\n",
       "      <td>0.155756+0.000000j</td>\n",
       "      <td>0.148057+0.000000j</td>\n",
       "      <td>0.140340-0.000000j</td>\n",
       "      <td>0.132606+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.040201-0.000000j</td>\n",
       "      <td>0.073410+0.000000j</td>\n",
       "      <td>0.107661+0.000000j</td>\n",
       "      <td>0.141347-0.000000j</td>\n",
       "      <td>0.173516-0.000000j</td>\n",
       "      <td>0.203384-0.000000j</td>\n",
       "      <td>0.230377-0.000000j</td>\n",
       "      <td>0.254147+0.000000j</td>\n",
       "      <td>0.274568+0.000000j</td>\n",
       "      <td>0.291696-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.201422+0.000000j</td>\n",
       "      <td>0.193886+0.000000j</td>\n",
       "      <td>0.186317-0.000000j</td>\n",
       "      <td>0.178717+0.000000j</td>\n",
       "      <td>0.171088+0.000000j</td>\n",
       "      <td>0.163434+0.000000j</td>\n",
       "      <td>0.155756+0.000000j</td>\n",
       "      <td>0.148057+0.000000j</td>\n",
       "      <td>0.140340-0.000000j</td>\n",
       "      <td>0.132606+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.058944+0.000000j</td>\n",
       "      <td>-0.107815-0.000000j</td>\n",
       "      <td>-0.158159-0.000000j</td>\n",
       "      <td>-0.207754+0.000000j</td>\n",
       "      <td>-0.255279-0.000000j</td>\n",
       "      <td>-0.299612+0.000000j</td>\n",
       "      <td>-0.339916+0.000000j</td>\n",
       "      <td>-0.375688-0.000000j</td>\n",
       "      <td>-0.406740-0.000000j</td>\n",
       "      <td>-0.433136-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.334833+0.000000j</td>\n",
       "      <td>-0.323955+0.000000j</td>\n",
       "      <td>-0.312985+0.000000j</td>\n",
       "      <td>-0.301929+0.000000j</td>\n",
       "      <td>-0.290790+0.000000j</td>\n",
       "      <td>-0.279573-0.000000j</td>\n",
       "      <td>-0.268281-0.000000j</td>\n",
       "      <td>-0.256919+0.000000j</td>\n",
       "      <td>-0.245491+0.000000j</td>\n",
       "      <td>-0.234000+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.040201+0.000000j</td>\n",
       "      <td>-0.073410+0.000000j</td>\n",
       "      <td>-0.107661+0.000000j</td>\n",
       "      <td>-0.141347+0.000000j</td>\n",
       "      <td>-0.173516+0.000000j</td>\n",
       "      <td>-0.203384+0.000000j</td>\n",
       "      <td>-0.230377-0.000000j</td>\n",
       "      <td>-0.254147+0.000000j</td>\n",
       "      <td>-0.274568+0.000000j</td>\n",
       "      <td>-0.291696+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.201422+0.000000j</td>\n",
       "      <td>-0.193886+0.000000j</td>\n",
       "      <td>-0.186317+0.000000j</td>\n",
       "      <td>-0.178717+0.000000j</td>\n",
       "      <td>-0.171088+0.000000j</td>\n",
       "      <td>-0.163434+0.000000j</td>\n",
       "      <td>-0.155756+0.000000j</td>\n",
       "      <td>-0.148057+0.000000j</td>\n",
       "      <td>-0.140340+0.000000j</td>\n",
       "      <td>-0.132606+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.040201-0.000000j</td>\n",
       "      <td>-0.073410-0.000000j</td>\n",
       "      <td>-0.107661-0.000000j</td>\n",
       "      <td>-0.141347-0.000000j</td>\n",
       "      <td>-0.173516-0.000000j</td>\n",
       "      <td>-0.203384-0.000000j</td>\n",
       "      <td>-0.230377-0.000000j</td>\n",
       "      <td>-0.254147-0.000000j</td>\n",
       "      <td>-0.274568-0.000000j</td>\n",
       "      <td>-0.291696-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.201422-0.000000j</td>\n",
       "      <td>-0.193886-0.000000j</td>\n",
       "      <td>-0.186317-0.000000j</td>\n",
       "      <td>-0.178717-0.000000j</td>\n",
       "      <td>-0.171088-0.000000j</td>\n",
       "      <td>-0.163434-0.000000j</td>\n",
       "      <td>-0.155756-0.000000j</td>\n",
       "      <td>-0.148057-0.000000j</td>\n",
       "      <td>-0.140340-0.000000j</td>\n",
       "      <td>-0.132606-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">8</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.000181-0.000000j</td>\n",
       "      <td>0.000676+0.000000j</td>\n",
       "      <td>0.001048+0.000000j</td>\n",
       "      <td>0.001561-0.000000j</td>\n",
       "      <td>0.002372-0.000000j</td>\n",
       "      <td>0.003530-0.000000j</td>\n",
       "      <td>0.005048-0.000000j</td>\n",
       "      <td>0.006928+0.000000j</td>\n",
       "      <td>0.009163+0.000000j</td>\n",
       "      <td>0.011727-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084583+0.000000j</td>\n",
       "      <td>0.085252-0.000000j</td>\n",
       "      <td>0.085848-0.000000j</td>\n",
       "      <td>0.086372-0.000000j</td>\n",
       "      <td>0.086827+0.000000j</td>\n",
       "      <td>0.087214+0.000000j</td>\n",
       "      <td>0.087535+0.000000j</td>\n",
       "      <td>0.087790+0.000000j</td>\n",
       "      <td>0.087983+0.000000j</td>\n",
       "      <td>0.088113+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.000115-0.000000j</td>\n",
       "      <td>-0.000427-0.000000j</td>\n",
       "      <td>-0.000665+0.000000j</td>\n",
       "      <td>-0.000996-0.000000j</td>\n",
       "      <td>-0.001512-0.000000j</td>\n",
       "      <td>-0.002244-0.000000j</td>\n",
       "      <td>-0.003199-0.000000j</td>\n",
       "      <td>-0.004381-0.000000j</td>\n",
       "      <td>-0.005784-0.000000j</td>\n",
       "      <td>-0.007393-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051392-0.000000j</td>\n",
       "      <td>-0.051726-0.000000j</td>\n",
       "      <td>-0.052010+0.000000j</td>\n",
       "      <td>-0.052248+0.000000j</td>\n",
       "      <td>-0.052440+0.000000j</td>\n",
       "      <td>-0.052587+0.000000j</td>\n",
       "      <td>-0.052690+0.000000j</td>\n",
       "      <td>-0.052750-0.000000j</td>\n",
       "      <td>-0.052766+0.000000j</td>\n",
       "      <td>-0.052741-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>-0.000115-0.000000j</td>\n",
       "      <td>-0.000427-0.000000j</td>\n",
       "      <td>-0.000665+0.000000j</td>\n",
       "      <td>-0.000996-0.000000j</td>\n",
       "      <td>-0.001512-0.000000j</td>\n",
       "      <td>-0.002244-0.000000j</td>\n",
       "      <td>-0.003199-0.000000j</td>\n",
       "      <td>-0.004381-0.000000j</td>\n",
       "      <td>-0.005784-0.000000j</td>\n",
       "      <td>-0.007393-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051392-0.000000j</td>\n",
       "      <td>-0.051726-0.000000j</td>\n",
       "      <td>-0.052010+0.000000j</td>\n",
       "      <td>-0.052248+0.000000j</td>\n",
       "      <td>-0.052440+0.000000j</td>\n",
       "      <td>-0.052587+0.000000j</td>\n",
       "      <td>-0.052690+0.000000j</td>\n",
       "      <td>-0.052750-0.000000j</td>\n",
       "      <td>-0.052766+0.000000j</td>\n",
       "      <td>-0.052741-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.000181-0.000000j</td>\n",
       "      <td>0.000676+0.000000j</td>\n",
       "      <td>0.001048+0.000000j</td>\n",
       "      <td>0.001561-0.000000j</td>\n",
       "      <td>0.002372-0.000000j</td>\n",
       "      <td>0.003530-0.000000j</td>\n",
       "      <td>0.005048-0.000000j</td>\n",
       "      <td>0.006928-0.000000j</td>\n",
       "      <td>0.009163-0.000000j</td>\n",
       "      <td>0.011727-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084583+0.000000j</td>\n",
       "      <td>0.085252+0.000000j</td>\n",
       "      <td>0.085848+0.000000j</td>\n",
       "      <td>0.086372+0.000000j</td>\n",
       "      <td>0.086827+0.000000j</td>\n",
       "      <td>0.087214+0.000000j</td>\n",
       "      <td>0.087535+0.000000j</td>\n",
       "      <td>0.087790+0.000000j</td>\n",
       "      <td>0.087983-0.000000j</td>\n",
       "      <td>0.088113+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.000115-0.000000j</td>\n",
       "      <td>0.000427-0.000000j</td>\n",
       "      <td>0.000665-0.000000j</td>\n",
       "      <td>0.000996-0.000000j</td>\n",
       "      <td>0.001512-0.000000j</td>\n",
       "      <td>0.002244-0.000000j</td>\n",
       "      <td>0.003199-0.000000j</td>\n",
       "      <td>0.004381-0.000000j</td>\n",
       "      <td>0.005784-0.000000j</td>\n",
       "      <td>0.007393-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051392-0.000000j</td>\n",
       "      <td>0.051726-0.000000j</td>\n",
       "      <td>0.052010-0.000000j</td>\n",
       "      <td>0.052248-0.000000j</td>\n",
       "      <td>0.052440-0.000000j</td>\n",
       "      <td>0.052587-0.000000j</td>\n",
       "      <td>0.052690+0.000000j</td>\n",
       "      <td>0.052750-0.000000j</td>\n",
       "      <td>0.052766-0.000000j</td>\n",
       "      <td>0.052741-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>0.000115-0.000000j</td>\n",
       "      <td>0.000427+0.000000j</td>\n",
       "      <td>0.000665-0.000000j</td>\n",
       "      <td>0.000996+0.000000j</td>\n",
       "      <td>0.001512+0.000000j</td>\n",
       "      <td>0.002244+0.000000j</td>\n",
       "      <td>0.003199+0.000000j</td>\n",
       "      <td>0.004381-0.000000j</td>\n",
       "      <td>0.005784+0.000000j</td>\n",
       "      <td>0.007393+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051392+0.000000j</td>\n",
       "      <td>0.051726+0.000000j</td>\n",
       "      <td>0.052010+0.000000j</td>\n",
       "      <td>0.052248+0.000000j</td>\n",
       "      <td>0.052440+0.000000j</td>\n",
       "      <td>0.052587+0.000000j</td>\n",
       "      <td>0.052690+0.000000j</td>\n",
       "      <td>0.052750+0.000000j</td>\n",
       "      <td>0.052766+0.000000j</td>\n",
       "      <td>0.052741+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">10</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000134-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008993-0.000000j</td>\n",
       "      <td>-0.009385+0.000000j</td>\n",
       "      <td>-0.009786+0.000000j</td>\n",
       "      <td>-0.010196+0.000000j</td>\n",
       "      <td>-0.010616+0.000000j</td>\n",
       "      <td>-0.011047+0.000000j</td>\n",
       "      <td>-0.011490-0.000000j</td>\n",
       "      <td>-0.011946+0.000000j</td>\n",
       "      <td>-0.012415-0.000000j</td>\n",
       "      <td>-0.012898-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005321+0.000000j</td>\n",
       "      <td>0.005549-0.000000j</td>\n",
       "      <td>0.005782-0.000000j</td>\n",
       "      <td>0.006019-0.000000j</td>\n",
       "      <td>0.006262+0.000000j</td>\n",
       "      <td>0.006512+0.000000j</td>\n",
       "      <td>0.006767+0.000000j</td>\n",
       "      <td>0.007030+0.000000j</td>\n",
       "      <td>0.007299+0.000000j</td>\n",
       "      <td>0.007577+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005321+0.000000j</td>\n",
       "      <td>0.005549-0.000000j</td>\n",
       "      <td>0.005782-0.000000j</td>\n",
       "      <td>0.006019-0.000000j</td>\n",
       "      <td>0.006262+0.000000j</td>\n",
       "      <td>0.006512+0.000000j</td>\n",
       "      <td>0.006767+0.000000j</td>\n",
       "      <td>0.007030+0.000000j</td>\n",
       "      <td>0.007299+0.000000j</td>\n",
       "      <td>0.007577+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000134-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008993+0.000000j</td>\n",
       "      <td>-0.009385+0.000000j</td>\n",
       "      <td>-0.009786-0.000000j</td>\n",
       "      <td>-0.010196+0.000000j</td>\n",
       "      <td>-0.010616+0.000000j</td>\n",
       "      <td>-0.011047-0.000000j</td>\n",
       "      <td>-0.011490+0.000000j</td>\n",
       "      <td>-0.011946+0.000000j</td>\n",
       "      <td>-0.012415+0.000000j</td>\n",
       "      <td>-0.012898+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005321+0.000000j</td>\n",
       "      <td>-0.005549+0.000000j</td>\n",
       "      <td>-0.005782+0.000000j</td>\n",
       "      <td>-0.006019+0.000000j</td>\n",
       "      <td>-0.006262+0.000000j</td>\n",
       "      <td>-0.006512+0.000000j</td>\n",
       "      <td>-0.006767+0.000000j</td>\n",
       "      <td>-0.007030+0.000000j</td>\n",
       "      <td>-0.007299+0.000000j</td>\n",
       "      <td>-0.007577+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005321-0.000000j</td>\n",
       "      <td>-0.005549-0.000000j</td>\n",
       "      <td>-0.005782-0.000000j</td>\n",
       "      <td>-0.006019-0.000000j</td>\n",
       "      <td>-0.006262-0.000000j</td>\n",
       "      <td>-0.006512-0.000000j</td>\n",
       "      <td>-0.006767-0.000000j</td>\n",
       "      <td>-0.007030-0.000000j</td>\n",
       "      <td>-0.007299-0.000000j</td>\n",
       "      <td>-0.007577-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">12</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000625+0.000000j</td>\n",
       "      <td>0.000674-0.000000j</td>\n",
       "      <td>0.000726-0.000000j</td>\n",
       "      <td>0.000781-0.000000j</td>\n",
       "      <td>0.000839+0.000000j</td>\n",
       "      <td>0.000901-0.000000j</td>\n",
       "      <td>0.000967+0.000000j</td>\n",
       "      <td>0.001037+0.000000j</td>\n",
       "      <td>0.001111+0.000000j</td>\n",
       "      <td>0.001190+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000362+0.000000j</td>\n",
       "      <td>-0.000390+0.000000j</td>\n",
       "      <td>-0.000419+0.000000j</td>\n",
       "      <td>-0.000451+0.000000j</td>\n",
       "      <td>-0.000484+0.000000j</td>\n",
       "      <td>-0.000520+0.000000j</td>\n",
       "      <td>-0.000558-0.000000j</td>\n",
       "      <td>-0.000598-0.000000j</td>\n",
       "      <td>-0.000640-0.000000j</td>\n",
       "      <td>-0.000686-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000362+0.000000j</td>\n",
       "      <td>-0.000390+0.000000j</td>\n",
       "      <td>-0.000419+0.000000j</td>\n",
       "      <td>-0.000451+0.000000j</td>\n",
       "      <td>-0.000484+0.000000j</td>\n",
       "      <td>-0.000520+0.000000j</td>\n",
       "      <td>-0.000558-0.000000j</td>\n",
       "      <td>-0.000598-0.000000j</td>\n",
       "      <td>-0.000640-0.000000j</td>\n",
       "      <td>-0.000686-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000625+0.000000j</td>\n",
       "      <td>0.000674+0.000000j</td>\n",
       "      <td>0.000726+0.000000j</td>\n",
       "      <td>0.000781-0.000000j</td>\n",
       "      <td>0.000839+0.000000j</td>\n",
       "      <td>0.000901+0.000000j</td>\n",
       "      <td>0.000967+0.000000j</td>\n",
       "      <td>0.001037+0.000000j</td>\n",
       "      <td>0.001111-0.000000j</td>\n",
       "      <td>0.001190+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000362-0.000000j</td>\n",
       "      <td>0.000390-0.000000j</td>\n",
       "      <td>0.000419-0.000000j</td>\n",
       "      <td>0.000451-0.000000j</td>\n",
       "      <td>0.000484-0.000000j</td>\n",
       "      <td>0.000520+0.000000j</td>\n",
       "      <td>0.000558-0.000000j</td>\n",
       "      <td>0.000598-0.000000j</td>\n",
       "      <td>0.000640-0.000000j</td>\n",
       "      <td>0.000686-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>SG</th>\n",
       "      <th>PU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000362+0.000000j</td>\n",
       "      <td>0.000390+0.000000j</td>\n",
       "      <td>0.000419+0.000000j</td>\n",
       "      <td>0.000451+0.000000j</td>\n",
       "      <td>0.000484+0.000000j</td>\n",
       "      <td>0.000520+0.000000j</td>\n",
       "      <td>0.000558+0.000000j</td>\n",
       "      <td>0.000598+0.000000j</td>\n",
       "      <td>0.000640+0.000000j</td>\n",
       "      <td>0.000686+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">SU</th>\n",
       "      <th>0</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>SU</th>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>SU</th>\n",
       "      <td>0.784764-0.000000j</td>\n",
       "      <td>0.292636+0.000000j</td>\n",
       "      <td>0.110286-0.000000j</td>\n",
       "      <td>0.047655+0.000000j</td>\n",
       "      <td>0.053351-0.000000j</td>\n",
       "      <td>0.100478-0.000000j</td>\n",
       "      <td>0.172894-0.000000j</td>\n",
       "      <td>0.260212-0.000000j</td>\n",
       "      <td>0.355478-0.000000j</td>\n",
       "      <td>0.453965-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.307136+0.000000j</td>\n",
       "      <td>0.353015-0.000000j</td>\n",
       "      <td>0.404836+0.000000j</td>\n",
       "      <td>0.459405-0.000000j</td>\n",
       "      <td>0.514462-0.000000j</td>\n",
       "      <td>0.568485+0.000000j</td>\n",
       "      <td>0.620505+0.000000j</td>\n",
       "      <td>0.669947-0.000000j</td>\n",
       "      <td>0.716512-0.000000j</td>\n",
       "      <td>0.760083+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>SU</th>\n",
       "      <td>-0.040643+0.000000j</td>\n",
       "      <td>-0.472493-0.000000j</td>\n",
       "      <td>-0.596717-0.000000j</td>\n",
       "      <td>-0.603036-0.000000j</td>\n",
       "      <td>-0.543465+0.000000j</td>\n",
       "      <td>-0.445631+0.000000j</td>\n",
       "      <td>-0.326262-0.000000j</td>\n",
       "      <td>-0.196158-0.000000j</td>\n",
       "      <td>-0.062516+0.000000j</td>\n",
       "      <td>0.069865-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.569503-0.000000j</td>\n",
       "      <td>-0.517657+0.000000j</td>\n",
       "      <td>-0.458173-0.000000j</td>\n",
       "      <td>-0.395217-0.000000j</td>\n",
       "      <td>-0.331713-0.000000j</td>\n",
       "      <td>-0.269614-0.000000j</td>\n",
       "      <td>-0.210149-0.000000j</td>\n",
       "      <td>-0.154034-0.000000j</td>\n",
       "      <td>-0.101629-0.000000j</td>\n",
       "      <td>-0.053060+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>SU</th>\n",
       "      <td>0.067996+0.000000j</td>\n",
       "      <td>0.124559+0.000000j</td>\n",
       "      <td>0.181056-0.000000j</td>\n",
       "      <td>0.236287+0.000000j</td>\n",
       "      <td>0.289141-0.000000j</td>\n",
       "      <td>0.338700-0.000000j</td>\n",
       "      <td>0.384294-0.000000j</td>\n",
       "      <td>0.425467-0.000000j</td>\n",
       "      <td>0.461963-0.000000j</td>\n",
       "      <td>0.493736-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012438-0.000000j</td>\n",
       "      <td>-0.026005+0.000000j</td>\n",
       "      <td>-0.036082-0.000000j</td>\n",
       "      <td>-0.043618+0.000000j</td>\n",
       "      <td>-0.049363-0.000000j</td>\n",
       "      <td>-0.053889+0.000000j</td>\n",
       "      <td>-0.057618-0.000000j</td>\n",
       "      <td>-0.060854-0.000000j</td>\n",
       "      <td>-0.063810-0.000000j</td>\n",
       "      <td>-0.066634+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>SU</th>\n",
       "      <td>-0.000240-0.000000j</td>\n",
       "      <td>-0.000812-0.000000j</td>\n",
       "      <td>-0.001227-0.000000j</td>\n",
       "      <td>-0.001840-0.000000j</td>\n",
       "      <td>-0.002813+0.000000j</td>\n",
       "      <td>-0.004167-0.000000j</td>\n",
       "      <td>-0.005886+0.000000j</td>\n",
       "      <td>-0.007949-0.000000j</td>\n",
       "      <td>-0.010322-0.000000j</td>\n",
       "      <td>-0.012952-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016562-0.000000j</td>\n",
       "      <td>0.000498+0.000000j</td>\n",
       "      <td>-0.014200+0.000000j</td>\n",
       "      <td>-0.027442-0.000000j</td>\n",
       "      <td>-0.039244+0.000000j</td>\n",
       "      <td>-0.049687+0.000000j</td>\n",
       "      <td>-0.058880+0.000000j</td>\n",
       "      <td>-0.066945-0.000000j</td>\n",
       "      <td>-0.074001+0.000000j</td>\n",
       "      <td>-0.080160-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>SU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000137-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036612+0.000000j</td>\n",
       "      <td>0.039931+0.000000j</td>\n",
       "      <td>0.042889+0.000000j</td>\n",
       "      <td>0.045534-0.000000j</td>\n",
       "      <td>0.047912+0.000000j</td>\n",
       "      <td>0.050071+0.000000j</td>\n",
       "      <td>0.052050+0.000000j</td>\n",
       "      <td>0.053883+0.000000j</td>\n",
       "      <td>0.055598+0.000000j</td>\n",
       "      <td>0.057219+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>SU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006051-0.000000j</td>\n",
       "      <td>-0.006437+0.000000j</td>\n",
       "      <td>-0.006788-0.000000j</td>\n",
       "      <td>-0.007113+0.000000j</td>\n",
       "      <td>-0.007416+0.000000j</td>\n",
       "      <td>-0.007706-0.000000j</td>\n",
       "      <td>-0.007985+0.000000j</td>\n",
       "      <td>-0.008259+0.000000j</td>\n",
       "      <td>-0.008530+0.000000j</td>\n",
       "      <td>-0.008801-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <th>SG</th>\n",
       "      <th>SU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000422+0.000000j</td>\n",
       "      <td>0.000449+0.000000j</td>\n",
       "      <td>0.000476+0.000000j</td>\n",
       "      <td>0.000501+0.000000j</td>\n",
       "      <td>0.000525+0.000000j</td>\n",
       "      <td>0.000549+0.000000j</td>\n",
       "      <td>0.000573+0.000000j</td>\n",
       "      <td>0.000598+0.000000j</td>\n",
       "      <td>0.000622+0.000000j</td>\n",
       "      <td>0.000648+0.000000j</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Eke                                         0.1                 1.1   \\\n",
       "Cont L  Labels M  Targ Total                                           \n",
       "PU   0  x       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "        y       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "     2  x       0 SG   PU    -0.272711-0.000000j -0.638474-0.000000j   \n",
       "               -2 SG   PU     0.512102-0.000000j  0.586763+0.000000j   \n",
       "                2 SG   PU     0.512102-0.000000j  0.586763+0.000000j   \n",
       "        y       0 SG   PU    -0.272711+0.000000j -0.638474-0.000000j   \n",
       "               -2 SG   PU    -0.512102+0.000000j -0.586763+0.000000j   \n",
       "                2 SG   PU    -0.512102-0.000000j -0.586763-0.000000j   \n",
       "     4  x       0 SG   PU    -0.059474-0.000000j  0.271217+0.000000j   \n",
       "               -2 SG   PU     0.080503-0.000000j -0.153387-0.000000j   \n",
       "                2 SG   PU     0.080503-0.000000j -0.153387-0.000000j   \n",
       "        y       0 SG   PU    -0.059474-0.000000j  0.271217+0.000000j   \n",
       "               -2 SG   PU    -0.080503+0.000000j  0.153387-0.000000j   \n",
       "                2 SG   PU    -0.080503+0.000000j  0.153387+0.000000j   \n",
       "     6  x       0 SG   PU    -0.058944+0.000000j -0.107815-0.000000j   \n",
       "               -2 SG   PU     0.040201-0.000000j  0.073410+0.000000j   \n",
       "                2 SG   PU     0.040201-0.000000j  0.073410+0.000000j   \n",
       "        y       0 SG   PU    -0.058944+0.000000j -0.107815-0.000000j   \n",
       "               -2 SG   PU    -0.040201+0.000000j -0.073410+0.000000j   \n",
       "                2 SG   PU    -0.040201-0.000000j -0.073410-0.000000j   \n",
       "     8  x       0 SG   PU     0.000181-0.000000j  0.000676+0.000000j   \n",
       "               -2 SG   PU    -0.000115-0.000000j -0.000427-0.000000j   \n",
       "                2 SG   PU    -0.000115-0.000000j -0.000427-0.000000j   \n",
       "        y       0 SG   PU     0.000181-0.000000j  0.000676+0.000000j   \n",
       "               -2 SG   PU     0.000115-0.000000j  0.000427-0.000000j   \n",
       "                2 SG   PU     0.000115-0.000000j  0.000427+0.000000j   \n",
       "     10 x       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "        y       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "     12 x       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "        y       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "SU   0  z       0 SG   SU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "     2  z       0 SG   SU     0.784764-0.000000j  0.292636+0.000000j   \n",
       "     4  z       0 SG   SU    -0.040643+0.000000j -0.472493-0.000000j   \n",
       "     6  z       0 SG   SU     0.067996+0.000000j  0.124559+0.000000j   \n",
       "     8  z       0 SG   SU    -0.000240-0.000000j -0.000812-0.000000j   \n",
       "     10 z       0 SG   SU                    NaN                 NaN   \n",
       "     12 z       0 SG   SU                    NaN                 NaN   \n",
       "     14 z       0 SG   SU                    NaN                 NaN   \n",
       "\n",
       "Eke                                         2.1                 3.1   \\\n",
       "Cont L  Labels M  Targ Total                                           \n",
       "PU   0  x       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "        y       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "     2  x       0 SG   PU    -0.775003-0.000000j -0.814436-0.000000j   \n",
       "               -2 SG   PU     0.614632-0.000000j  0.622681-0.000000j   \n",
       "                2 SG   PU     0.614632-0.000000j  0.622681-0.000000j   \n",
       "        y       0 SG   PU    -0.775003-0.000000j -0.814436-0.000000j   \n",
       "               -2 SG   PU    -0.614632+0.000000j -0.622681+0.000000j   \n",
       "                2 SG   PU    -0.614632-0.000000j -0.622681-0.000000j   \n",
       "     4  x       0 SG   PU     0.432955-0.000000j  0.521218-0.000000j   \n",
       "               -2 SG   PU    -0.252803-0.000000j -0.294656-0.000000j   \n",
       "                2 SG   PU    -0.252803-0.000000j -0.294656-0.000000j   \n",
       "        y       0 SG   PU     0.432955+0.000000j  0.521218-0.000000j   \n",
       "               -2 SG   PU     0.252803-0.000000j  0.294656-0.000000j   \n",
       "                2 SG   PU     0.252803+0.000000j  0.294656+0.000000j   \n",
       "     6  x       0 SG   PU    -0.158159+0.000000j -0.207754-0.000000j   \n",
       "               -2 SG   PU     0.107661+0.000000j  0.141347-0.000000j   \n",
       "                2 SG   PU     0.107661+0.000000j  0.141347-0.000000j   \n",
       "        y       0 SG   PU    -0.158159-0.000000j -0.207754+0.000000j   \n",
       "               -2 SG   PU    -0.107661+0.000000j -0.141347+0.000000j   \n",
       "                2 SG   PU    -0.107661-0.000000j -0.141347-0.000000j   \n",
       "     8  x       0 SG   PU     0.001048+0.000000j  0.001561-0.000000j   \n",
       "               -2 SG   PU    -0.000665+0.000000j -0.000996-0.000000j   \n",
       "                2 SG   PU    -0.000665+0.000000j -0.000996-0.000000j   \n",
       "        y       0 SG   PU     0.001048+0.000000j  0.001561-0.000000j   \n",
       "               -2 SG   PU     0.000665-0.000000j  0.000996-0.000000j   \n",
       "                2 SG   PU     0.000665-0.000000j  0.000996+0.000000j   \n",
       "     10 x       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "        y       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "     12 x       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "        y       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "SU   0  z       0 SG   SU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "     2  z       0 SG   SU     0.110286-0.000000j  0.047655+0.000000j   \n",
       "     4  z       0 SG   SU    -0.596717-0.000000j -0.603036-0.000000j   \n",
       "     6  z       0 SG   SU     0.181056-0.000000j  0.236287+0.000000j   \n",
       "     8  z       0 SG   SU    -0.001227-0.000000j -0.001840-0.000000j   \n",
       "     10 z       0 SG   SU                    NaN                 NaN   \n",
       "     12 z       0 SG   SU                    NaN                 NaN   \n",
       "     14 z       0 SG   SU                    NaN                 NaN   \n",
       "\n",
       "Eke                                         4.1                 5.1   \\\n",
       "Cont L  Labels M  Targ Total                                           \n",
       "PU   0  x       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "        y       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "     2  x       0 SG   PU    -0.794373+0.000000j -0.736632+0.000000j   \n",
       "               -2 SG   PU     0.618586-0.000000j  0.606800-0.000000j   \n",
       "                2 SG   PU     0.618586-0.000000j  0.606800-0.000000j   \n",
       "        y       0 SG   PU    -0.794373+0.000000j -0.736632-0.000000j   \n",
       "               -2 SG   PU    -0.618586+0.000000j -0.606800+0.000000j   \n",
       "                2 SG   PU    -0.618586-0.000000j -0.606800-0.000000j   \n",
       "     4  x       0 SG   PU     0.562256-0.000000j  0.570921-0.000000j   \n",
       "               -2 SG   PU    -0.300540-0.000000j -0.282866+0.000000j   \n",
       "                2 SG   PU    -0.300540-0.000000j -0.282866+0.000000j   \n",
       "        y       0 SG   PU     0.562256-0.000000j  0.570921-0.000000j   \n",
       "               -2 SG   PU     0.300540-0.000000j  0.282866-0.000000j   \n",
       "                2 SG   PU     0.300540+0.000000j  0.282866+0.000000j   \n",
       "     6  x       0 SG   PU    -0.255279-0.000000j -0.299612+0.000000j   \n",
       "               -2 SG   PU     0.173516-0.000000j  0.203384-0.000000j   \n",
       "                2 SG   PU     0.173516-0.000000j  0.203384-0.000000j   \n",
       "        y       0 SG   PU    -0.255279-0.000000j -0.299612+0.000000j   \n",
       "               -2 SG   PU    -0.173516+0.000000j -0.203384+0.000000j   \n",
       "                2 SG   PU    -0.173516-0.000000j -0.203384-0.000000j   \n",
       "     8  x       0 SG   PU     0.002372-0.000000j  0.003530-0.000000j   \n",
       "               -2 SG   PU    -0.001512-0.000000j -0.002244-0.000000j   \n",
       "                2 SG   PU    -0.001512-0.000000j -0.002244-0.000000j   \n",
       "        y       0 SG   PU     0.002372-0.000000j  0.003530-0.000000j   \n",
       "               -2 SG   PU     0.001512-0.000000j  0.002244-0.000000j   \n",
       "                2 SG   PU     0.001512+0.000000j  0.002244+0.000000j   \n",
       "     10 x       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "        y       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "     12 x       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "        y       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "SU   0  z       0 SG   SU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "     2  z       0 SG   SU     0.053351-0.000000j  0.100478-0.000000j   \n",
       "     4  z       0 SG   SU    -0.543465+0.000000j -0.445631+0.000000j   \n",
       "     6  z       0 SG   SU     0.289141-0.000000j  0.338700-0.000000j   \n",
       "     8  z       0 SG   SU    -0.002813+0.000000j -0.004167-0.000000j   \n",
       "     10 z       0 SG   SU                    NaN                 NaN   \n",
       "     12 z       0 SG   SU                    NaN                 NaN   \n",
       "     14 z       0 SG   SU                    NaN                 NaN   \n",
       "\n",
       "Eke                                         6.1                 7.1   \\\n",
       "Cont L  Labels M  Targ Total                                           \n",
       "PU   0  x       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "        y       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "     2  x       0 SG   PU    -0.655845-0.000000j -0.562321-0.000000j   \n",
       "               -2 SG   PU     0.590309-0.000000j  0.571219+0.000000j   \n",
       "                2 SG   PU     0.590309-0.000000j  0.571219+0.000000j   \n",
       "        y       0 SG   PU    -0.655845-0.000000j -0.562321-0.000000j   \n",
       "               -2 SG   PU    -0.590309+0.000000j -0.571219+0.000000j   \n",
       "                2 SG   PU    -0.590309-0.000000j -0.571219-0.000000j   \n",
       "     4  x       0 SG   PU     0.557098-0.000000j  0.527855+0.000000j   \n",
       "               -2 SG   PU    -0.249932-0.000000j -0.207619-0.000000j   \n",
       "                2 SG   PU    -0.249932-0.000000j -0.207619-0.000000j   \n",
       "        y       0 SG   PU     0.557098+0.000000j  0.527855-0.000000j   \n",
       "               -2 SG   PU     0.249932-0.000000j  0.207619-0.000000j   \n",
       "                2 SG   PU     0.249932+0.000000j  0.207619+0.000000j   \n",
       "     6  x       0 SG   PU    -0.339916-0.000000j -0.375688-0.000000j   \n",
       "               -2 SG   PU     0.230377-0.000000j  0.254147+0.000000j   \n",
       "                2 SG   PU     0.230377-0.000000j  0.254147+0.000000j   \n",
       "        y       0 SG   PU    -0.339916+0.000000j -0.375688-0.000000j   \n",
       "               -2 SG   PU    -0.230377-0.000000j -0.254147+0.000000j   \n",
       "                2 SG   PU    -0.230377-0.000000j -0.254147-0.000000j   \n",
       "     8  x       0 SG   PU     0.005048-0.000000j  0.006928+0.000000j   \n",
       "               -2 SG   PU    -0.003199-0.000000j -0.004381-0.000000j   \n",
       "                2 SG   PU    -0.003199-0.000000j -0.004381-0.000000j   \n",
       "        y       0 SG   PU     0.005048-0.000000j  0.006928-0.000000j   \n",
       "               -2 SG   PU     0.003199-0.000000j  0.004381-0.000000j   \n",
       "                2 SG   PU     0.003199+0.000000j  0.004381-0.000000j   \n",
       "     10 x       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "        y       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "     12 x       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "        y       0 SG   PU                    NaN                 NaN   \n",
       "               -2 SG   PU                    NaN                 NaN   \n",
       "                2 SG   PU                    NaN                 NaN   \n",
       "SU   0  z       0 SG   SU     1.000000-0.000000j  1.000000-0.000000j   \n",
       "     2  z       0 SG   SU     0.172894-0.000000j  0.260212-0.000000j   \n",
       "     4  z       0 SG   SU    -0.326262-0.000000j -0.196158-0.000000j   \n",
       "     6  z       0 SG   SU     0.384294-0.000000j  0.425467-0.000000j   \n",
       "     8  z       0 SG   SU    -0.005886+0.000000j -0.007949-0.000000j   \n",
       "     10 z       0 SG   SU                    NaN                 NaN   \n",
       "     12 z       0 SG   SU                    NaN                 NaN   \n",
       "     14 z       0 SG   SU                    NaN                 NaN   \n",
       "\n",
       "Eke                                         8.1                 9.1   ...  \\\n",
       "Cont L  Labels M  Targ Total                                          ...   \n",
       "PU   0  x       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j  ...   \n",
       "        y       0 SG   PU     1.000000-0.000000j  1.000000-0.000000j  ...   \n",
       "     2  x       0 SG   PU    -0.463340-0.000000j -0.363902-0.000000j  ...   \n",
       "               -2 SG   PU     0.551014+0.000000j  0.530717-0.000000j  ...   \n",
       "                2 SG   PU     0.551014+0.000000j  0.530717-0.000000j  ...   \n",
       "        y       0 SG   PU    -0.463340-0.000000j -0.363902-0.000000j  ...   \n",
       "               -2 SG   PU    -0.551014+0.000000j -0.530717+0.000000j  ...   \n",
       "                2 SG   PU    -0.551014-0.000000j -0.530717-0.000000j  ...   \n",
       "     4  x       0 SG   PU     0.488402+0.000000j  0.442583-0.000000j  ...   \n",
       "               -2 SG   PU    -0.160148-0.000000j -0.110502-0.000000j  ...   \n",
       "                2 SG   PU    -0.160148-0.000000j -0.110502-0.000000j  ...   \n",
       "        y       0 SG   PU     0.488402-0.000000j  0.442583-0.000000j  ...   \n",
       "               -2 SG   PU     0.160148-0.000000j  0.110502-0.000000j  ...   \n",
       "                2 SG   PU     0.160148+0.000000j  0.110502+0.000000j  ...   \n",
       "     6  x       0 SG   PU    -0.406740-0.000000j -0.433136-0.000000j  ...   \n",
       "               -2 SG   PU     0.274568+0.000000j  0.291696-0.000000j  ...   \n",
       "                2 SG   PU     0.274568+0.000000j  0.291696-0.000000j  ...   \n",
       "        y       0 SG   PU    -0.406740-0.000000j -0.433136-0.000000j  ...   \n",
       "               -2 SG   PU    -0.274568+0.000000j -0.291696+0.000000j  ...   \n",
       "                2 SG   PU    -0.274568-0.000000j -0.291696-0.000000j  ...   \n",
       "     8  x       0 SG   PU     0.009163+0.000000j  0.011727-0.000000j  ...   \n",
       "               -2 SG   PU    -0.005784-0.000000j -0.007393-0.000000j  ...   \n",
       "                2 SG   PU    -0.005784-0.000000j -0.007393-0.000000j  ...   \n",
       "        y       0 SG   PU     0.009163-0.000000j  0.011727-0.000000j  ...   \n",
       "               -2 SG   PU     0.005784-0.000000j  0.007393-0.000000j  ...   \n",
       "                2 SG   PU     0.005784+0.000000j  0.007393+0.000000j  ...   \n",
       "     10 x       0 SG   PU                    NaN -0.000134-0.000000j  ...   \n",
       "               -2 SG   PU                    NaN                 NaN  ...   \n",
       "                2 SG   PU                    NaN                 NaN  ...   \n",
       "        y       0 SG   PU                    NaN -0.000134-0.000000j  ...   \n",
       "               -2 SG   PU                    NaN                 NaN  ...   \n",
       "                2 SG   PU                    NaN                 NaN  ...   \n",
       "     12 x       0 SG   PU                    NaN                 NaN  ...   \n",
       "               -2 SG   PU                    NaN                 NaN  ...   \n",
       "                2 SG   PU                    NaN                 NaN  ...   \n",
       "        y       0 SG   PU                    NaN                 NaN  ...   \n",
       "               -2 SG   PU                    NaN                 NaN  ...   \n",
       "                2 SG   PU                    NaN                 NaN  ...   \n",
       "SU   0  z       0 SG   SU     1.000000-0.000000j  1.000000-0.000000j  ...   \n",
       "     2  z       0 SG   SU     0.355478-0.000000j  0.453965-0.000000j  ...   \n",
       "     4  z       0 SG   SU    -0.062516+0.000000j  0.069865-0.000000j  ...   \n",
       "     6  z       0 SG   SU     0.461963-0.000000j  0.493736-0.000000j  ...   \n",
       "     8  z       0 SG   SU    -0.010322-0.000000j -0.012952-0.000000j  ...   \n",
       "     10 z       0 SG   SU                    NaN  0.000137-0.000000j  ...   \n",
       "     12 z       0 SG   SU                    NaN                 NaN  ...   \n",
       "     14 z       0 SG   SU                    NaN                 NaN  ...   \n",
       "\n",
       "Eke                                         41.1                42.1  \\\n",
       "Cont L  Labels M  Targ Total                                           \n",
       "PU   0  x       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "        y       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "     2  x       0 SG   PU     0.655141+0.000000j  0.650741+0.000000j   \n",
       "               -2 SG   PU     0.322705+0.000000j  0.323603+0.000000j   \n",
       "                2 SG   PU     0.322705+0.000000j  0.323603+0.000000j   \n",
       "        y       0 SG   PU     0.655141+0.000000j  0.650741-0.000000j   \n",
       "               -2 SG   PU    -0.322705+0.000000j -0.323603+0.000000j   \n",
       "                2 SG   PU    -0.322705-0.000000j -0.323603-0.000000j   \n",
       "     4  x       0 SG   PU    -0.522724+0.000000j -0.532915+0.000000j   \n",
       "               -2 SG   PU     0.555041+0.000000j  0.556708+0.000000j   \n",
       "                2 SG   PU     0.555041+0.000000j  0.556708+0.000000j   \n",
       "        y       0 SG   PU    -0.522724+0.000000j -0.532915+0.000000j   \n",
       "               -2 SG   PU    -0.555041+0.000000j -0.556708+0.000000j   \n",
       "                2 SG   PU    -0.555041-0.000000j -0.556708-0.000000j   \n",
       "     6  x       0 SG   PU    -0.334833+0.000000j -0.323955+0.000000j   \n",
       "               -2 SG   PU     0.201422+0.000000j  0.193886+0.000000j   \n",
       "                2 SG   PU     0.201422+0.000000j  0.193886+0.000000j   \n",
       "        y       0 SG   PU    -0.334833+0.000000j -0.323955+0.000000j   \n",
       "               -2 SG   PU    -0.201422+0.000000j -0.193886+0.000000j   \n",
       "                2 SG   PU    -0.201422-0.000000j -0.193886-0.000000j   \n",
       "     8  x       0 SG   PU     0.084583+0.000000j  0.085252-0.000000j   \n",
       "               -2 SG   PU    -0.051392-0.000000j -0.051726-0.000000j   \n",
       "                2 SG   PU    -0.051392-0.000000j -0.051726-0.000000j   \n",
       "        y       0 SG   PU     0.084583+0.000000j  0.085252+0.000000j   \n",
       "               -2 SG   PU     0.051392-0.000000j  0.051726-0.000000j   \n",
       "                2 SG   PU     0.051392+0.000000j  0.051726+0.000000j   \n",
       "     10 x       0 SG   PU    -0.008993-0.000000j -0.009385+0.000000j   \n",
       "               -2 SG   PU     0.005321+0.000000j  0.005549-0.000000j   \n",
       "                2 SG   PU     0.005321+0.000000j  0.005549-0.000000j   \n",
       "        y       0 SG   PU    -0.008993+0.000000j -0.009385+0.000000j   \n",
       "               -2 SG   PU    -0.005321+0.000000j -0.005549+0.000000j   \n",
       "                2 SG   PU    -0.005321-0.000000j -0.005549-0.000000j   \n",
       "     12 x       0 SG   PU     0.000625+0.000000j  0.000674-0.000000j   \n",
       "               -2 SG   PU    -0.000362+0.000000j -0.000390+0.000000j   \n",
       "                2 SG   PU    -0.000362+0.000000j -0.000390+0.000000j   \n",
       "        y       0 SG   PU     0.000625+0.000000j  0.000674+0.000000j   \n",
       "               -2 SG   PU     0.000362-0.000000j  0.000390-0.000000j   \n",
       "                2 SG   PU     0.000362+0.000000j  0.000390+0.000000j   \n",
       "SU   0  z       0 SG   SU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "     2  z       0 SG   SU     0.307136+0.000000j  0.353015-0.000000j   \n",
       "     4  z       0 SG   SU    -0.569503-0.000000j -0.517657+0.000000j   \n",
       "     6  z       0 SG   SU    -0.012438-0.000000j -0.026005+0.000000j   \n",
       "     8  z       0 SG   SU     0.016562-0.000000j  0.000498+0.000000j   \n",
       "     10 z       0 SG   SU     0.036612+0.000000j  0.039931+0.000000j   \n",
       "     12 z       0 SG   SU    -0.006051-0.000000j -0.006437+0.000000j   \n",
       "     14 z       0 SG   SU     0.000422+0.000000j  0.000449+0.000000j   \n",
       "\n",
       "Eke                                         43.1                44.1  \\\n",
       "Cont L  Labels M  Targ Total                                           \n",
       "PU   0  x       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "        y       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "     2  x       0 SG   PU     0.645402-0.000000j  0.639166-0.000000j   \n",
       "               -2 SG   PU     0.324693-0.000000j  0.325966-0.000000j   \n",
       "                2 SG   PU     0.324693-0.000000j  0.325966-0.000000j   \n",
       "        y       0 SG   PU     0.645402+0.000000j  0.639166+0.000000j   \n",
       "               -2 SG   PU    -0.324693+0.000000j -0.325966+0.000000j   \n",
       "                2 SG   PU    -0.324693-0.000000j -0.325966-0.000000j   \n",
       "     4  x       0 SG   PU    -0.542405+0.000000j -0.551224+0.000000j   \n",
       "               -2 SG   PU     0.557821-0.000000j  0.558406+0.000000j   \n",
       "                2 SG   PU     0.557821-0.000000j  0.558406+0.000000j   \n",
       "        y       0 SG   PU    -0.542405-0.000000j -0.551224+0.000000j   \n",
       "               -2 SG   PU    -0.557821+0.000000j -0.558406+0.000000j   \n",
       "                2 SG   PU    -0.557821-0.000000j -0.558406-0.000000j   \n",
       "     6  x       0 SG   PU    -0.312985+0.000000j -0.301929+0.000000j   \n",
       "               -2 SG   PU     0.186317-0.000000j  0.178717-0.000000j   \n",
       "                2 SG   PU     0.186317-0.000000j  0.178717+0.000000j   \n",
       "        y       0 SG   PU    -0.312985+0.000000j -0.301929+0.000000j   \n",
       "               -2 SG   PU    -0.186317+0.000000j -0.178717+0.000000j   \n",
       "                2 SG   PU    -0.186317-0.000000j -0.178717-0.000000j   \n",
       "     8  x       0 SG   PU     0.085848-0.000000j  0.086372-0.000000j   \n",
       "               -2 SG   PU    -0.052010+0.000000j -0.052248+0.000000j   \n",
       "                2 SG   PU    -0.052010+0.000000j -0.052248+0.000000j   \n",
       "        y       0 SG   PU     0.085848+0.000000j  0.086372+0.000000j   \n",
       "               -2 SG   PU     0.052010-0.000000j  0.052248-0.000000j   \n",
       "                2 SG   PU     0.052010+0.000000j  0.052248+0.000000j   \n",
       "     10 x       0 SG   PU    -0.009786+0.000000j -0.010196+0.000000j   \n",
       "               -2 SG   PU     0.005782-0.000000j  0.006019-0.000000j   \n",
       "                2 SG   PU     0.005782-0.000000j  0.006019-0.000000j   \n",
       "        y       0 SG   PU    -0.009786-0.000000j -0.010196+0.000000j   \n",
       "               -2 SG   PU    -0.005782+0.000000j -0.006019+0.000000j   \n",
       "                2 SG   PU    -0.005782-0.000000j -0.006019-0.000000j   \n",
       "     12 x       0 SG   PU     0.000726-0.000000j  0.000781-0.000000j   \n",
       "               -2 SG   PU    -0.000419+0.000000j -0.000451+0.000000j   \n",
       "                2 SG   PU    -0.000419+0.000000j -0.000451+0.000000j   \n",
       "        y       0 SG   PU     0.000726+0.000000j  0.000781-0.000000j   \n",
       "               -2 SG   PU     0.000419-0.000000j  0.000451-0.000000j   \n",
       "                2 SG   PU     0.000419+0.000000j  0.000451+0.000000j   \n",
       "SU   0  z       0 SG   SU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "     2  z       0 SG   SU     0.404836+0.000000j  0.459405-0.000000j   \n",
       "     4  z       0 SG   SU    -0.458173-0.000000j -0.395217-0.000000j   \n",
       "     6  z       0 SG   SU    -0.036082-0.000000j -0.043618+0.000000j   \n",
       "     8  z       0 SG   SU    -0.014200+0.000000j -0.027442-0.000000j   \n",
       "     10 z       0 SG   SU     0.042889+0.000000j  0.045534-0.000000j   \n",
       "     12 z       0 SG   SU    -0.006788-0.000000j -0.007113+0.000000j   \n",
       "     14 z       0 SG   SU     0.000476+0.000000j  0.000501+0.000000j   \n",
       "\n",
       "Eke                                         45.1                46.1  \\\n",
       "Cont L  Labels M  Targ Total                                           \n",
       "PU   0  x       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "        y       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "     2  x       0 SG   PU     0.632075-0.000000j  0.624163+0.000000j   \n",
       "               -2 SG   PU     0.327414+0.000000j  0.329029+0.000000j   \n",
       "                2 SG   PU     0.327414+0.000000j  0.329029+0.000000j   \n",
       "        y       0 SG   PU     0.632075+0.000000j  0.624163+0.000000j   \n",
       "               -2 SG   PU    -0.327414+0.000000j -0.329029+0.000000j   \n",
       "                2 SG   PU    -0.327414-0.000000j -0.329029-0.000000j   \n",
       "     4  x       0 SG   PU    -0.559398+0.000000j -0.566951-0.000000j   \n",
       "               -2 SG   PU     0.558483+0.000000j  0.558074+0.000000j   \n",
       "                2 SG   PU     0.558483+0.000000j  0.558074+0.000000j   \n",
       "        y       0 SG   PU    -0.559398+0.000000j -0.566951-0.000000j   \n",
       "               -2 SG   PU    -0.558483+0.000000j -0.558074+0.000000j   \n",
       "                2 SG   PU    -0.558483-0.000000j -0.558074-0.000000j   \n",
       "     6  x       0 SG   PU    -0.290790+0.000000j -0.279573+0.000000j   \n",
       "               -2 SG   PU     0.171088+0.000000j  0.163434+0.000000j   \n",
       "                2 SG   PU     0.171088+0.000000j  0.163434+0.000000j   \n",
       "        y       0 SG   PU    -0.290790+0.000000j -0.279573-0.000000j   \n",
       "               -2 SG   PU    -0.171088+0.000000j -0.163434+0.000000j   \n",
       "                2 SG   PU    -0.171088-0.000000j -0.163434-0.000000j   \n",
       "     8  x       0 SG   PU     0.086827+0.000000j  0.087214+0.000000j   \n",
       "               -2 SG   PU    -0.052440+0.000000j -0.052587+0.000000j   \n",
       "                2 SG   PU    -0.052440+0.000000j -0.052587+0.000000j   \n",
       "        y       0 SG   PU     0.086827+0.000000j  0.087214+0.000000j   \n",
       "               -2 SG   PU     0.052440-0.000000j  0.052587-0.000000j   \n",
       "                2 SG   PU     0.052440+0.000000j  0.052587+0.000000j   \n",
       "     10 x       0 SG   PU    -0.010616+0.000000j -0.011047+0.000000j   \n",
       "               -2 SG   PU     0.006262+0.000000j  0.006512+0.000000j   \n",
       "                2 SG   PU     0.006262+0.000000j  0.006512+0.000000j   \n",
       "        y       0 SG   PU    -0.010616+0.000000j -0.011047-0.000000j   \n",
       "               -2 SG   PU    -0.006262+0.000000j -0.006512+0.000000j   \n",
       "                2 SG   PU    -0.006262-0.000000j -0.006512-0.000000j   \n",
       "     12 x       0 SG   PU     0.000839+0.000000j  0.000901-0.000000j   \n",
       "               -2 SG   PU    -0.000484+0.000000j -0.000520+0.000000j   \n",
       "                2 SG   PU    -0.000484+0.000000j -0.000520+0.000000j   \n",
       "        y       0 SG   PU     0.000839+0.000000j  0.000901+0.000000j   \n",
       "               -2 SG   PU     0.000484-0.000000j  0.000520+0.000000j   \n",
       "                2 SG   PU     0.000484+0.000000j  0.000520+0.000000j   \n",
       "SU   0  z       0 SG   SU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "     2  z       0 SG   SU     0.514462-0.000000j  0.568485+0.000000j   \n",
       "     4  z       0 SG   SU    -0.331713-0.000000j -0.269614-0.000000j   \n",
       "     6  z       0 SG   SU    -0.049363-0.000000j -0.053889+0.000000j   \n",
       "     8  z       0 SG   SU    -0.039244+0.000000j -0.049687+0.000000j   \n",
       "     10 z       0 SG   SU     0.047912+0.000000j  0.050071+0.000000j   \n",
       "     12 z       0 SG   SU    -0.007416+0.000000j -0.007706-0.000000j   \n",
       "     14 z       0 SG   SU     0.000525+0.000000j  0.000549+0.000000j   \n",
       "\n",
       "Eke                                         47.1                48.1  \\\n",
       "Cont L  Labels M  Targ Total                                           \n",
       "PU   0  x       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "        y       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "     2  x       0 SG   PU     0.615465+0.000000j  0.606012+0.000000j   \n",
       "               -2 SG   PU     0.330804+0.000000j  0.332734+0.000000j   \n",
       "                2 SG   PU     0.330804+0.000000j  0.332734+0.000000j   \n",
       "        y       0 SG   PU     0.615465+0.000000j  0.606012-0.000000j   \n",
       "               -2 SG   PU    -0.330804+0.000000j -0.332734+0.000000j   \n",
       "                2 SG   PU    -0.330804-0.000000j -0.332734-0.000000j   \n",
       "     4  x       0 SG   PU    -0.573903-0.000000j -0.580274-0.000000j   \n",
       "               -2 SG   PU     0.557198-0.000000j  0.555870+0.000000j   \n",
       "                2 SG   PU     0.557198-0.000000j  0.555870+0.000000j   \n",
       "        y       0 SG   PU    -0.573903-0.000000j -0.580274-0.000000j   \n",
       "               -2 SG   PU    -0.557198+0.000000j -0.555870+0.000000j   \n",
       "                2 SG   PU    -0.557198-0.000000j -0.555870-0.000000j   \n",
       "     6  x       0 SG   PU    -0.268281+0.000000j -0.256919+0.000000j   \n",
       "               -2 SG   PU     0.155756+0.000000j  0.148057+0.000000j   \n",
       "                2 SG   PU     0.155756+0.000000j  0.148057+0.000000j   \n",
       "        y       0 SG   PU    -0.268281-0.000000j -0.256919+0.000000j   \n",
       "               -2 SG   PU    -0.155756+0.000000j -0.148057+0.000000j   \n",
       "                2 SG   PU    -0.155756-0.000000j -0.148057-0.000000j   \n",
       "     8  x       0 SG   PU     0.087535+0.000000j  0.087790+0.000000j   \n",
       "               -2 SG   PU    -0.052690+0.000000j -0.052750-0.000000j   \n",
       "                2 SG   PU    -0.052690+0.000000j -0.052750-0.000000j   \n",
       "        y       0 SG   PU     0.087535+0.000000j  0.087790+0.000000j   \n",
       "               -2 SG   PU     0.052690+0.000000j  0.052750-0.000000j   \n",
       "                2 SG   PU     0.052690+0.000000j  0.052750+0.000000j   \n",
       "     10 x       0 SG   PU    -0.011490-0.000000j -0.011946+0.000000j   \n",
       "               -2 SG   PU     0.006767+0.000000j  0.007030+0.000000j   \n",
       "                2 SG   PU     0.006767+0.000000j  0.007030+0.000000j   \n",
       "        y       0 SG   PU    -0.011490+0.000000j -0.011946+0.000000j   \n",
       "               -2 SG   PU    -0.006767+0.000000j -0.007030+0.000000j   \n",
       "                2 SG   PU    -0.006767-0.000000j -0.007030-0.000000j   \n",
       "     12 x       0 SG   PU     0.000967+0.000000j  0.001037+0.000000j   \n",
       "               -2 SG   PU    -0.000558-0.000000j -0.000598-0.000000j   \n",
       "                2 SG   PU    -0.000558-0.000000j -0.000598-0.000000j   \n",
       "        y       0 SG   PU     0.000967+0.000000j  0.001037+0.000000j   \n",
       "               -2 SG   PU     0.000558-0.000000j  0.000598-0.000000j   \n",
       "                2 SG   PU     0.000558+0.000000j  0.000598+0.000000j   \n",
       "SU   0  z       0 SG   SU     1.000000+0.000000j  1.000000+0.000000j   \n",
       "     2  z       0 SG   SU     0.620505+0.000000j  0.669947-0.000000j   \n",
       "     4  z       0 SG   SU    -0.210149-0.000000j -0.154034-0.000000j   \n",
       "     6  z       0 SG   SU    -0.057618-0.000000j -0.060854-0.000000j   \n",
       "     8  z       0 SG   SU    -0.058880+0.000000j -0.066945-0.000000j   \n",
       "     10 z       0 SG   SU     0.052050+0.000000j  0.053883+0.000000j   \n",
       "     12 z       0 SG   SU    -0.007985+0.000000j -0.008259+0.000000j   \n",
       "     14 z       0 SG   SU     0.000573+0.000000j  0.000598+0.000000j   \n",
       "\n",
       "Eke                                         49.1                50.1  \n",
       "Cont L  Labels M  Targ Total                                          \n",
       "PU   0  x       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j  \n",
       "        y       0 SG   PU     1.000000+0.000000j  1.000000+0.000000j  \n",
       "     2  x       0 SG   PU     0.595832+0.000000j  0.584952+0.000000j  \n",
       "               -2 SG   PU     0.334812+0.000000j  0.337033+0.000000j  \n",
       "                2 SG   PU     0.334812+0.000000j  0.337033+0.000000j  \n",
       "        y       0 SG   PU     0.595832-0.000000j  0.584952+0.000000j  \n",
       "               -2 SG   PU    -0.334812+0.000000j -0.337033+0.000000j  \n",
       "                2 SG   PU    -0.334812-0.000000j -0.337033-0.000000j  \n",
       "     4  x       0 SG   PU    -0.586083+0.000000j -0.591343+0.000000j  \n",
       "               -2 SG   PU     0.554108+0.000000j  0.551925+0.000000j  \n",
       "                2 SG   PU     0.554108+0.000000j  0.551925+0.000000j  \n",
       "        y       0 SG   PU    -0.586083+0.000000j -0.591343+0.000000j  \n",
       "               -2 SG   PU    -0.554108+0.000000j -0.551925+0.000000j  \n",
       "                2 SG   PU    -0.554108-0.000000j -0.551925-0.000000j  \n",
       "     6  x       0 SG   PU    -0.245491-0.000000j -0.234000+0.000000j  \n",
       "               -2 SG   PU     0.140340-0.000000j  0.132606+0.000000j  \n",
       "                2 SG   PU     0.140340-0.000000j  0.132606+0.000000j  \n",
       "        y       0 SG   PU    -0.245491+0.000000j -0.234000+0.000000j  \n",
       "               -2 SG   PU    -0.140340+0.000000j -0.132606+0.000000j  \n",
       "                2 SG   PU    -0.140340-0.000000j -0.132606-0.000000j  \n",
       "     8  x       0 SG   PU     0.087983+0.000000j  0.088113+0.000000j  \n",
       "               -2 SG   PU    -0.052766+0.000000j -0.052741-0.000000j  \n",
       "                2 SG   PU    -0.052766+0.000000j -0.052741-0.000000j  \n",
       "        y       0 SG   PU     0.087983-0.000000j  0.088113+0.000000j  \n",
       "               -2 SG   PU     0.052766-0.000000j  0.052741-0.000000j  \n",
       "                2 SG   PU     0.052766+0.000000j  0.052741+0.000000j  \n",
       "     10 x       0 SG   PU    -0.012415-0.000000j -0.012898-0.000000j  \n",
       "               -2 SG   PU     0.007299+0.000000j  0.007577+0.000000j  \n",
       "                2 SG   PU     0.007299+0.000000j  0.007577+0.000000j  \n",
       "        y       0 SG   PU    -0.012415+0.000000j -0.012898+0.000000j  \n",
       "               -2 SG   PU    -0.007299+0.000000j -0.007577+0.000000j  \n",
       "                2 SG   PU    -0.007299-0.000000j -0.007577-0.000000j  \n",
       "     12 x       0 SG   PU     0.001111+0.000000j  0.001190+0.000000j  \n",
       "               -2 SG   PU    -0.000640-0.000000j -0.000686-0.000000j  \n",
       "                2 SG   PU    -0.000640-0.000000j -0.000686-0.000000j  \n",
       "        y       0 SG   PU     0.001111-0.000000j  0.001190+0.000000j  \n",
       "               -2 SG   PU     0.000640-0.000000j  0.000686-0.000000j  \n",
       "                2 SG   PU     0.000640+0.000000j  0.000686+0.000000j  \n",
       "SU   0  z       0 SG   SU     1.000000+0.000000j  1.000000+0.000000j  \n",
       "     2  z       0 SG   SU     0.716512-0.000000j  0.760083+0.000000j  \n",
       "     4  z       0 SG   SU    -0.101629-0.000000j -0.053060+0.000000j  \n",
       "     6  z       0 SG   SU    -0.063810-0.000000j -0.066634+0.000000j  \n",
       "     8  z       0 SG   SU    -0.074001+0.000000j -0.080160-0.000000j  \n",
       "     10 z       0 SG   SU     0.055598+0.000000j  0.057219+0.000000j  \n",
       "     12 z       0 SG   SU    -0.008530+0.000000j -0.008801-0.000000j  \n",
       "     14 z       0 SG   SU     0.000622+0.000000j  0.000648+0.000000j  \n",
       "\n",
       "[46 rows x 51 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabulate complex values...\n",
    "mTermSTpd, _ = ep.util.multiDimXrToPD(mTermST, colDims='Eke', thres = thres)\n",
    "mTermSTpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here all the $\\beta_{LM}$ are normalised, hence the X-sections are unity. For the $N_2$ case ([linear, $D_{\\infty h}$ symmetry](https://en.wikipedia.org/wiki/Molecular_symmetry)), note the correlation of symmetry groups with polarization geometries (SU > z; PU > x,y).\n",
    "\n",
    "As a further test, we can check abs terms x pol == y pol - there should only be a rotation (phase) difference between the results for these polarization geometries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min difference: -3.3306690738754696e-16, max difference: 3.3306690738754696e-16\n"
     ]
    }
   ],
   "source": [
    "testSub = mTermST.sel({'Labels':'x'}).pipe(np.abs) - mTermST.sel({'Labels':'y'}).pipe(np.abs)\n",
    "\n",
    "print(f\"Min difference: {testSub.min().data}, max difference: {testSub.max().data}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The X-sections are stored sepearately in the output Xarray:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files (x86)\\Microsoft Visual Studio\\Shared\\Anaconda3_64\\envs\\ePSdev\\lib\\site-packages\\xarray\\plot\\plot.py:86: FutureWarning:\n",
      "\n",
      "This DataArray contains multi-dimensional coordinates. In the future, these coordinates will be transposed as well unless you specify transpose_coords=False.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1159.12x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mTermST.XS.real.squeeze().plot.line(x='Eke', col='Labels');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With symmetry summation\n",
    "\n",
    "This produces simpler results, summed over continua."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time = 3.079366445541382 seconds, for 51 energy points, 3 polarizations, threshold=0.0001.\n"
     ]
    }
   ],
   "source": [
    "symSum = True  # Sum over symmetry groups, or keep separate?\n",
    "\n",
    "start = time.time()\n",
    "mTermST, mTermS, mTermTest = ep.geomFunc.mfblmXprod(dataSet[0], QNs = None, thres = thres, selDims = {'it':1, 'Type':'L'}, thresDims='Eke', RX=RX, symSum=symSum, SFflag=True, phaseConvention=phaseConvention)\n",
    "end = time.time()\n",
    "print('Elapsed time = {0} seconds, for {1} energy points, {2} polarizations, threshold={3}.'.format((end-start), mTermST.Eke.size, RX.size, thres))\n",
    "\n",
    "# Elapsed time = 1.704383134841919 seconds, for 51 energy points, 3 polarizations, threshold=threshold=0.01.\n",
    "# Elapsed time = 3.151679754257202 seconds, for 51 energy points, 3 polarizations, threshold=0.0001."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting data n2_3sg_0.1-50.1eV_A2.inp.out, pType=r, thres=0.01, with Seaborn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Full results (before summation)\n",
    "mTermST.attrs['dataType'] = 'matE'  # Set matE here to allow for correct plotting of sym dims.\n",
    "\n",
    "plotDimsRed = ['Labels','L','M']  # Set plotDims to fix dim ordering in plot\n",
    "if not symSum:\n",
    "    plotDimsRed.extend(['Cont','Targ','Total'])\n",
    "    \n",
    "daPlot, daPlotpd, legendList, gFig = ep.lmPlot(mTermST, plotDims=plotDimsRed, xDim='Eke', sumDims=None, pType = 'r', thres = 0.01, fillna = True, SFflag=False)  \n",
    "\n",
    "# daPlot, daPlotpd, legendList, gFig = ep.lmPlot(mTermST, xDim='Eke', sumDims=None, pType = 'r', thres = 0.01, fillna = True)  # If plotDims is not passed use default ordering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Eke</th>\n",
       "      <th>0.1</th>\n",
       "      <th>1.1</th>\n",
       "      <th>2.1</th>\n",
       "      <th>3.1</th>\n",
       "      <th>4.1</th>\n",
       "      <th>5.1</th>\n",
       "      <th>6.1</th>\n",
       "      <th>7.1</th>\n",
       "      <th>8.1</th>\n",
       "      <th>9.1</th>\n",
       "      <th>...</th>\n",
       "      <th>41.1</th>\n",
       "      <th>42.1</th>\n",
       "      <th>43.1</th>\n",
       "      <th>44.1</th>\n",
       "      <th>45.1</th>\n",
       "      <th>46.1</th>\n",
       "      <th>47.1</th>\n",
       "      <th>48.1</th>\n",
       "      <th>49.1</th>\n",
       "      <th>50.1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <th>Labels</th>\n",
       "      <th>M</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
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       "      <td>1.000000-0.000000j</td>\n",
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       "      <td>1.000000-0.000000j</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
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       "      <th>x</th>\n",
       "      <th>0</th>\n",
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       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th>0</th>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>1.000000-0.000000j</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
       "      <td>1.000000+0.000000j</td>\n",
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       "      <th rowspan=\"7\" valign=\"top\">2</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
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       "      <td>0.292636+0.000000j</td>\n",
       "      <td>0.110286-0.000000j</td>\n",
       "      <td>0.047655+0.000000j</td>\n",
       "      <td>0.053351-0.000000j</td>\n",
       "      <td>0.100478-0.000000j</td>\n",
       "      <td>0.172894-0.000000j</td>\n",
       "      <td>0.260212-0.000000j</td>\n",
       "      <td>0.355478-0.000000j</td>\n",
       "      <td>0.453965-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.307136+0.000000j</td>\n",
       "      <td>0.353015-0.000000j</td>\n",
       "      <td>0.404836+0.000000j</td>\n",
       "      <td>0.459405-0.000000j</td>\n",
       "      <td>0.514462-0.000000j</td>\n",
       "      <td>0.568485+0.000000j</td>\n",
       "      <td>0.620505+0.000000j</td>\n",
       "      <td>0.669947-0.000000j</td>\n",
       "      <td>0.716512-0.000000j</td>\n",
       "      <td>0.760083+0.000000j</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.272711-0.000000j</td>\n",
       "      <td>-0.638474-0.000000j</td>\n",
       "      <td>-0.775003-0.000000j</td>\n",
       "      <td>-0.814436-0.000000j</td>\n",
       "      <td>-0.794373+0.000000j</td>\n",
       "      <td>-0.736632+0.000000j</td>\n",
       "      <td>-0.655845-0.000000j</td>\n",
       "      <td>-0.562321-0.000000j</td>\n",
       "      <td>-0.463340-0.000000j</td>\n",
       "      <td>-0.363902-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.655141+0.000000j</td>\n",
       "      <td>0.650741+0.000000j</td>\n",
       "      <td>0.645402-0.000000j</td>\n",
       "      <td>0.639166-0.000000j</td>\n",
       "      <td>0.632075-0.000000j</td>\n",
       "      <td>0.624163+0.000000j</td>\n",
       "      <td>0.615465+0.000000j</td>\n",
       "      <td>0.606012+0.000000j</td>\n",
       "      <td>0.595832+0.000000j</td>\n",
       "      <td>0.584952+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>0.512102-0.000000j</td>\n",
       "      <td>0.586763+0.000000j</td>\n",
       "      <td>0.614632-0.000000j</td>\n",
       "      <td>0.622681-0.000000j</td>\n",
       "      <td>0.618586-0.000000j</td>\n",
       "      <td>0.606800-0.000000j</td>\n",
       "      <td>0.590309-0.000000j</td>\n",
       "      <td>0.571219+0.000000j</td>\n",
       "      <td>0.551014+0.000000j</td>\n",
       "      <td>0.530717-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322705+0.000000j</td>\n",
       "      <td>0.323603+0.000000j</td>\n",
       "      <td>0.324693-0.000000j</td>\n",
       "      <td>0.325966-0.000000j</td>\n",
       "      <td>0.327414+0.000000j</td>\n",
       "      <td>0.329029+0.000000j</td>\n",
       "      <td>0.330804+0.000000j</td>\n",
       "      <td>0.332734+0.000000j</td>\n",
       "      <td>0.334812+0.000000j</td>\n",
       "      <td>0.337033+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.512102-0.000000j</td>\n",
       "      <td>0.586763+0.000000j</td>\n",
       "      <td>0.614632-0.000000j</td>\n",
       "      <td>0.622681-0.000000j</td>\n",
       "      <td>0.618586-0.000000j</td>\n",
       "      <td>0.606800-0.000000j</td>\n",
       "      <td>0.590309-0.000000j</td>\n",
       "      <td>0.571219+0.000000j</td>\n",
       "      <td>0.551014+0.000000j</td>\n",
       "      <td>0.530717-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.322705+0.000000j</td>\n",
       "      <td>0.323603+0.000000j</td>\n",
       "      <td>0.324693-0.000000j</td>\n",
       "      <td>0.325966-0.000000j</td>\n",
       "      <td>0.327414+0.000000j</td>\n",
       "      <td>0.329029+0.000000j</td>\n",
       "      <td>0.330804+0.000000j</td>\n",
       "      <td>0.332734+0.000000j</td>\n",
       "      <td>0.334812+0.000000j</td>\n",
       "      <td>0.337033+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.272711+0.000000j</td>\n",
       "      <td>-0.638474-0.000000j</td>\n",
       "      <td>-0.775003-0.000000j</td>\n",
       "      <td>-0.814436-0.000000j</td>\n",
       "      <td>-0.794373+0.000000j</td>\n",
       "      <td>-0.736632-0.000000j</td>\n",
       "      <td>-0.655845-0.000000j</td>\n",
       "      <td>-0.562321-0.000000j</td>\n",
       "      <td>-0.463340-0.000000j</td>\n",
       "      <td>-0.363902-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.655141+0.000000j</td>\n",
       "      <td>0.650741-0.000000j</td>\n",
       "      <td>0.645402+0.000000j</td>\n",
       "      <td>0.639166+0.000000j</td>\n",
       "      <td>0.632075+0.000000j</td>\n",
       "      <td>0.624163+0.000000j</td>\n",
       "      <td>0.615465+0.000000j</td>\n",
       "      <td>0.606012-0.000000j</td>\n",
       "      <td>0.595832-0.000000j</td>\n",
       "      <td>0.584952+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>-0.512102+0.000000j</td>\n",
       "      <td>-0.586763+0.000000j</td>\n",
       "      <td>-0.614632+0.000000j</td>\n",
       "      <td>-0.622681+0.000000j</td>\n",
       "      <td>-0.618586+0.000000j</td>\n",
       "      <td>-0.606800+0.000000j</td>\n",
       "      <td>-0.590309+0.000000j</td>\n",
       "      <td>-0.571219+0.000000j</td>\n",
       "      <td>-0.551014+0.000000j</td>\n",
       "      <td>-0.530717+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.322705+0.000000j</td>\n",
       "      <td>-0.323603+0.000000j</td>\n",
       "      <td>-0.324693+0.000000j</td>\n",
       "      <td>-0.325966+0.000000j</td>\n",
       "      <td>-0.327414+0.000000j</td>\n",
       "      <td>-0.329029+0.000000j</td>\n",
       "      <td>-0.330804+0.000000j</td>\n",
       "      <td>-0.332734+0.000000j</td>\n",
       "      <td>-0.334812+0.000000j</td>\n",
       "      <td>-0.337033+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.512102-0.000000j</td>\n",
       "      <td>-0.586763-0.000000j</td>\n",
       "      <td>-0.614632-0.000000j</td>\n",
       "      <td>-0.622681-0.000000j</td>\n",
       "      <td>-0.618586-0.000000j</td>\n",
       "      <td>-0.606800-0.000000j</td>\n",
       "      <td>-0.590309-0.000000j</td>\n",
       "      <td>-0.571219-0.000000j</td>\n",
       "      <td>-0.551014-0.000000j</td>\n",
       "      <td>-0.530717-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.322705-0.000000j</td>\n",
       "      <td>-0.323603-0.000000j</td>\n",
       "      <td>-0.324693-0.000000j</td>\n",
       "      <td>-0.325966-0.000000j</td>\n",
       "      <td>-0.327414-0.000000j</td>\n",
       "      <td>-0.329029-0.000000j</td>\n",
       "      <td>-0.330804-0.000000j</td>\n",
       "      <td>-0.332734-0.000000j</td>\n",
       "      <td>-0.334812-0.000000j</td>\n",
       "      <td>-0.337033-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">4</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.040643+0.000000j</td>\n",
       "      <td>-0.472493-0.000000j</td>\n",
       "      <td>-0.596717-0.000000j</td>\n",
       "      <td>-0.603036-0.000000j</td>\n",
       "      <td>-0.543465+0.000000j</td>\n",
       "      <td>-0.445631+0.000000j</td>\n",
       "      <td>-0.326262-0.000000j</td>\n",
       "      <td>-0.196158-0.000000j</td>\n",
       "      <td>-0.062516+0.000000j</td>\n",
       "      <td>0.069865-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.569503-0.000000j</td>\n",
       "      <td>-0.517657+0.000000j</td>\n",
       "      <td>-0.458173-0.000000j</td>\n",
       "      <td>-0.395217-0.000000j</td>\n",
       "      <td>-0.331713-0.000000j</td>\n",
       "      <td>-0.269614-0.000000j</td>\n",
       "      <td>-0.210149-0.000000j</td>\n",
       "      <td>-0.154034-0.000000j</td>\n",
       "      <td>-0.101629-0.000000j</td>\n",
       "      <td>-0.053060+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.059474-0.000000j</td>\n",
       "      <td>0.271217+0.000000j</td>\n",
       "      <td>0.432955-0.000000j</td>\n",
       "      <td>0.521218-0.000000j</td>\n",
       "      <td>0.562256-0.000000j</td>\n",
       "      <td>0.570921-0.000000j</td>\n",
       "      <td>0.557098-0.000000j</td>\n",
       "      <td>0.527855+0.000000j</td>\n",
       "      <td>0.488402+0.000000j</td>\n",
       "      <td>0.442583-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.522724+0.000000j</td>\n",
       "      <td>-0.532915+0.000000j</td>\n",
       "      <td>-0.542405+0.000000j</td>\n",
       "      <td>-0.551224+0.000000j</td>\n",
       "      <td>-0.559398+0.000000j</td>\n",
       "      <td>-0.566951-0.000000j</td>\n",
       "      <td>-0.573903-0.000000j</td>\n",
       "      <td>-0.580274-0.000000j</td>\n",
       "      <td>-0.586083+0.000000j</td>\n",
       "      <td>-0.591343+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>0.080503-0.000000j</td>\n",
       "      <td>-0.153387-0.000000j</td>\n",
       "      <td>-0.252803-0.000000j</td>\n",
       "      <td>-0.294656-0.000000j</td>\n",
       "      <td>-0.300540-0.000000j</td>\n",
       "      <td>-0.282866+0.000000j</td>\n",
       "      <td>-0.249932-0.000000j</td>\n",
       "      <td>-0.207619-0.000000j</td>\n",
       "      <td>-0.160148-0.000000j</td>\n",
       "      <td>-0.110502-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555041+0.000000j</td>\n",
       "      <td>0.556708+0.000000j</td>\n",
       "      <td>0.557821-0.000000j</td>\n",
       "      <td>0.558406+0.000000j</td>\n",
       "      <td>0.558483+0.000000j</td>\n",
       "      <td>0.558074+0.000000j</td>\n",
       "      <td>0.557198-0.000000j</td>\n",
       "      <td>0.555870+0.000000j</td>\n",
       "      <td>0.554108+0.000000j</td>\n",
       "      <td>0.551925+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.080503-0.000000j</td>\n",
       "      <td>-0.153387-0.000000j</td>\n",
       "      <td>-0.252803-0.000000j</td>\n",
       "      <td>-0.294656-0.000000j</td>\n",
       "      <td>-0.300540-0.000000j</td>\n",
       "      <td>-0.282866+0.000000j</td>\n",
       "      <td>-0.249932-0.000000j</td>\n",
       "      <td>-0.207619-0.000000j</td>\n",
       "      <td>-0.160148-0.000000j</td>\n",
       "      <td>-0.110502-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.555041+0.000000j</td>\n",
       "      <td>0.556708+0.000000j</td>\n",
       "      <td>0.557821-0.000000j</td>\n",
       "      <td>0.558406+0.000000j</td>\n",
       "      <td>0.558483+0.000000j</td>\n",
       "      <td>0.558074+0.000000j</td>\n",
       "      <td>0.557198-0.000000j</td>\n",
       "      <td>0.555870+0.000000j</td>\n",
       "      <td>0.554108+0.000000j</td>\n",
       "      <td>0.551925+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.059474-0.000000j</td>\n",
       "      <td>0.271217+0.000000j</td>\n",
       "      <td>0.432955+0.000000j</td>\n",
       "      <td>0.521218-0.000000j</td>\n",
       "      <td>0.562256-0.000000j</td>\n",
       "      <td>0.570921-0.000000j</td>\n",
       "      <td>0.557098+0.000000j</td>\n",
       "      <td>0.527855-0.000000j</td>\n",
       "      <td>0.488402-0.000000j</td>\n",
       "      <td>0.442583-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.522724+0.000000j</td>\n",
       "      <td>-0.532915+0.000000j</td>\n",
       "      <td>-0.542405-0.000000j</td>\n",
       "      <td>-0.551224+0.000000j</td>\n",
       "      <td>-0.559398+0.000000j</td>\n",
       "      <td>-0.566951-0.000000j</td>\n",
       "      <td>-0.573903-0.000000j</td>\n",
       "      <td>-0.580274-0.000000j</td>\n",
       "      <td>-0.586083+0.000000j</td>\n",
       "      <td>-0.591343+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>-0.080503+0.000000j</td>\n",
       "      <td>0.153387-0.000000j</td>\n",
       "      <td>0.252803-0.000000j</td>\n",
       "      <td>0.294656-0.000000j</td>\n",
       "      <td>0.300540-0.000000j</td>\n",
       "      <td>0.282866-0.000000j</td>\n",
       "      <td>0.249932-0.000000j</td>\n",
       "      <td>0.207619-0.000000j</td>\n",
       "      <td>0.160148-0.000000j</td>\n",
       "      <td>0.110502-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.555041+0.000000j</td>\n",
       "      <td>-0.556708+0.000000j</td>\n",
       "      <td>-0.557821+0.000000j</td>\n",
       "      <td>-0.558406+0.000000j</td>\n",
       "      <td>-0.558483+0.000000j</td>\n",
       "      <td>-0.558074+0.000000j</td>\n",
       "      <td>-0.557198+0.000000j</td>\n",
       "      <td>-0.555870+0.000000j</td>\n",
       "      <td>-0.554108+0.000000j</td>\n",
       "      <td>-0.551925+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.080503+0.000000j</td>\n",
       "      <td>0.153387+0.000000j</td>\n",
       "      <td>0.252803+0.000000j</td>\n",
       "      <td>0.294656+0.000000j</td>\n",
       "      <td>0.300540+0.000000j</td>\n",
       "      <td>0.282866+0.000000j</td>\n",
       "      <td>0.249932+0.000000j</td>\n",
       "      <td>0.207619+0.000000j</td>\n",
       "      <td>0.160148+0.000000j</td>\n",
       "      <td>0.110502+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.555041-0.000000j</td>\n",
       "      <td>-0.556708-0.000000j</td>\n",
       "      <td>-0.557821-0.000000j</td>\n",
       "      <td>-0.558406-0.000000j</td>\n",
       "      <td>-0.558483-0.000000j</td>\n",
       "      <td>-0.558074-0.000000j</td>\n",
       "      <td>-0.557198-0.000000j</td>\n",
       "      <td>-0.555870-0.000000j</td>\n",
       "      <td>-0.554108-0.000000j</td>\n",
       "      <td>-0.551925-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">6</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <td>0.067996+0.000000j</td>\n",
       "      <td>0.124559+0.000000j</td>\n",
       "      <td>0.181056-0.000000j</td>\n",
       "      <td>0.236287+0.000000j</td>\n",
       "      <td>0.289141-0.000000j</td>\n",
       "      <td>0.338700-0.000000j</td>\n",
       "      <td>0.384294-0.000000j</td>\n",
       "      <td>0.425467-0.000000j</td>\n",
       "      <td>0.461963-0.000000j</td>\n",
       "      <td>0.493736-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012438-0.000000j</td>\n",
       "      <td>-0.026005+0.000000j</td>\n",
       "      <td>-0.036082-0.000000j</td>\n",
       "      <td>-0.043618+0.000000j</td>\n",
       "      <td>-0.049363-0.000000j</td>\n",
       "      <td>-0.053889+0.000000j</td>\n",
       "      <td>-0.057618-0.000000j</td>\n",
       "      <td>-0.060854-0.000000j</td>\n",
       "      <td>-0.063810-0.000000j</td>\n",
       "      <td>-0.066634+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.058944+0.000000j</td>\n",
       "      <td>-0.107815-0.000000j</td>\n",
       "      <td>-0.158159+0.000000j</td>\n",
       "      <td>-0.207754-0.000000j</td>\n",
       "      <td>-0.255279-0.000000j</td>\n",
       "      <td>-0.299612+0.000000j</td>\n",
       "      <td>-0.339916-0.000000j</td>\n",
       "      <td>-0.375688-0.000000j</td>\n",
       "      <td>-0.406740-0.000000j</td>\n",
       "      <td>-0.433136-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.334833+0.000000j</td>\n",
       "      <td>-0.323955+0.000000j</td>\n",
       "      <td>-0.312985+0.000000j</td>\n",
       "      <td>-0.301929+0.000000j</td>\n",
       "      <td>-0.290790+0.000000j</td>\n",
       "      <td>-0.279573+0.000000j</td>\n",
       "      <td>-0.268281+0.000000j</td>\n",
       "      <td>-0.256919+0.000000j</td>\n",
       "      <td>-0.245491-0.000000j</td>\n",
       "      <td>-0.234000+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>0.040201-0.000000j</td>\n",
       "      <td>0.073410+0.000000j</td>\n",
       "      <td>0.107661+0.000000j</td>\n",
       "      <td>0.141347-0.000000j</td>\n",
       "      <td>0.173516-0.000000j</td>\n",
       "      <td>0.203384-0.000000j</td>\n",
       "      <td>0.230377-0.000000j</td>\n",
       "      <td>0.254147+0.000000j</td>\n",
       "      <td>0.274568+0.000000j</td>\n",
       "      <td>0.291696-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.201422+0.000000j</td>\n",
       "      <td>0.193886+0.000000j</td>\n",
       "      <td>0.186317-0.000000j</td>\n",
       "      <td>0.178717-0.000000j</td>\n",
       "      <td>0.171088+0.000000j</td>\n",
       "      <td>0.163434+0.000000j</td>\n",
       "      <td>0.155756+0.000000j</td>\n",
       "      <td>0.148057+0.000000j</td>\n",
       "      <td>0.140340-0.000000j</td>\n",
       "      <td>0.132606+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.040201-0.000000j</td>\n",
       "      <td>0.073410+0.000000j</td>\n",
       "      <td>0.107661+0.000000j</td>\n",
       "      <td>0.141347-0.000000j</td>\n",
       "      <td>0.173516-0.000000j</td>\n",
       "      <td>0.203384-0.000000j</td>\n",
       "      <td>0.230377-0.000000j</td>\n",
       "      <td>0.254147+0.000000j</td>\n",
       "      <td>0.274568+0.000000j</td>\n",
       "      <td>0.291696-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.201422+0.000000j</td>\n",
       "      <td>0.193886+0.000000j</td>\n",
       "      <td>0.186317-0.000000j</td>\n",
       "      <td>0.178717+0.000000j</td>\n",
       "      <td>0.171088+0.000000j</td>\n",
       "      <td>0.163434+0.000000j</td>\n",
       "      <td>0.155756+0.000000j</td>\n",
       "      <td>0.148057+0.000000j</td>\n",
       "      <td>0.140340-0.000000j</td>\n",
       "      <td>0.132606+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.058944+0.000000j</td>\n",
       "      <td>-0.107815-0.000000j</td>\n",
       "      <td>-0.158159-0.000000j</td>\n",
       "      <td>-0.207754+0.000000j</td>\n",
       "      <td>-0.255279-0.000000j</td>\n",
       "      <td>-0.299612+0.000000j</td>\n",
       "      <td>-0.339916+0.000000j</td>\n",
       "      <td>-0.375688-0.000000j</td>\n",
       "      <td>-0.406740-0.000000j</td>\n",
       "      <td>-0.433136-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.334833+0.000000j</td>\n",
       "      <td>-0.323955+0.000000j</td>\n",
       "      <td>-0.312985+0.000000j</td>\n",
       "      <td>-0.301929+0.000000j</td>\n",
       "      <td>-0.290790+0.000000j</td>\n",
       "      <td>-0.279573-0.000000j</td>\n",
       "      <td>-0.268281-0.000000j</td>\n",
       "      <td>-0.256919+0.000000j</td>\n",
       "      <td>-0.245491+0.000000j</td>\n",
       "      <td>-0.234000+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>-0.040201+0.000000j</td>\n",
       "      <td>-0.073410+0.000000j</td>\n",
       "      <td>-0.107661+0.000000j</td>\n",
       "      <td>-0.141347+0.000000j</td>\n",
       "      <td>-0.173516+0.000000j</td>\n",
       "      <td>-0.203384+0.000000j</td>\n",
       "      <td>-0.230377-0.000000j</td>\n",
       "      <td>-0.254147+0.000000j</td>\n",
       "      <td>-0.274568+0.000000j</td>\n",
       "      <td>-0.291696+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.201422+0.000000j</td>\n",
       "      <td>-0.193886+0.000000j</td>\n",
       "      <td>-0.186317+0.000000j</td>\n",
       "      <td>-0.178717+0.000000j</td>\n",
       "      <td>-0.171088+0.000000j</td>\n",
       "      <td>-0.163434+0.000000j</td>\n",
       "      <td>-0.155756+0.000000j</td>\n",
       "      <td>-0.148057+0.000000j</td>\n",
       "      <td>-0.140340+0.000000j</td>\n",
       "      <td>-0.132606+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.040201-0.000000j</td>\n",
       "      <td>-0.073410-0.000000j</td>\n",
       "      <td>-0.107661-0.000000j</td>\n",
       "      <td>-0.141347-0.000000j</td>\n",
       "      <td>-0.173516-0.000000j</td>\n",
       "      <td>-0.203384-0.000000j</td>\n",
       "      <td>-0.230377-0.000000j</td>\n",
       "      <td>-0.254147-0.000000j</td>\n",
       "      <td>-0.274568-0.000000j</td>\n",
       "      <td>-0.291696-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.201422-0.000000j</td>\n",
       "      <td>-0.193886-0.000000j</td>\n",
       "      <td>-0.186317-0.000000j</td>\n",
       "      <td>-0.178717-0.000000j</td>\n",
       "      <td>-0.171088-0.000000j</td>\n",
       "      <td>-0.163434-0.000000j</td>\n",
       "      <td>-0.155756-0.000000j</td>\n",
       "      <td>-0.148057-0.000000j</td>\n",
       "      <td>-0.140340-0.000000j</td>\n",
       "      <td>-0.132606-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">8</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <td>-0.000240-0.000000j</td>\n",
       "      <td>-0.000812-0.000000j</td>\n",
       "      <td>-0.001227-0.000000j</td>\n",
       "      <td>-0.001840-0.000000j</td>\n",
       "      <td>-0.002813+0.000000j</td>\n",
       "      <td>-0.004167-0.000000j</td>\n",
       "      <td>-0.005886+0.000000j</td>\n",
       "      <td>-0.007949-0.000000j</td>\n",
       "      <td>-0.010322-0.000000j</td>\n",
       "      <td>-0.012952-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016562-0.000000j</td>\n",
       "      <td>0.000498+0.000000j</td>\n",
       "      <td>-0.014200+0.000000j</td>\n",
       "      <td>-0.027442-0.000000j</td>\n",
       "      <td>-0.039244+0.000000j</td>\n",
       "      <td>-0.049687+0.000000j</td>\n",
       "      <td>-0.058880+0.000000j</td>\n",
       "      <td>-0.066945-0.000000j</td>\n",
       "      <td>-0.074001+0.000000j</td>\n",
       "      <td>-0.080160-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000181-0.000000j</td>\n",
       "      <td>0.000676+0.000000j</td>\n",
       "      <td>0.001048+0.000000j</td>\n",
       "      <td>0.001561-0.000000j</td>\n",
       "      <td>0.002372-0.000000j</td>\n",
       "      <td>0.003530-0.000000j</td>\n",
       "      <td>0.005048-0.000000j</td>\n",
       "      <td>0.006928+0.000000j</td>\n",
       "      <td>0.009163+0.000000j</td>\n",
       "      <td>0.011727-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084583+0.000000j</td>\n",
       "      <td>0.085252-0.000000j</td>\n",
       "      <td>0.085848-0.000000j</td>\n",
       "      <td>0.086372-0.000000j</td>\n",
       "      <td>0.086827+0.000000j</td>\n",
       "      <td>0.087214+0.000000j</td>\n",
       "      <td>0.087535+0.000000j</td>\n",
       "      <td>0.087790+0.000000j</td>\n",
       "      <td>0.087983+0.000000j</td>\n",
       "      <td>0.088113+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>-0.000115-0.000000j</td>\n",
       "      <td>-0.000427-0.000000j</td>\n",
       "      <td>-0.000665+0.000000j</td>\n",
       "      <td>-0.000996-0.000000j</td>\n",
       "      <td>-0.001512-0.000000j</td>\n",
       "      <td>-0.002244-0.000000j</td>\n",
       "      <td>-0.003199-0.000000j</td>\n",
       "      <td>-0.004381-0.000000j</td>\n",
       "      <td>-0.005784-0.000000j</td>\n",
       "      <td>-0.007393-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051392-0.000000j</td>\n",
       "      <td>-0.051726-0.000000j</td>\n",
       "      <td>-0.052010+0.000000j</td>\n",
       "      <td>-0.052248+0.000000j</td>\n",
       "      <td>-0.052440+0.000000j</td>\n",
       "      <td>-0.052587+0.000000j</td>\n",
       "      <td>-0.052690+0.000000j</td>\n",
       "      <td>-0.052750-0.000000j</td>\n",
       "      <td>-0.052766+0.000000j</td>\n",
       "      <td>-0.052741-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000115-0.000000j</td>\n",
       "      <td>-0.000427-0.000000j</td>\n",
       "      <td>-0.000665+0.000000j</td>\n",
       "      <td>-0.000996-0.000000j</td>\n",
       "      <td>-0.001512-0.000000j</td>\n",
       "      <td>-0.002244-0.000000j</td>\n",
       "      <td>-0.003199-0.000000j</td>\n",
       "      <td>-0.004381-0.000000j</td>\n",
       "      <td>-0.005784-0.000000j</td>\n",
       "      <td>-0.007393-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051392-0.000000j</td>\n",
       "      <td>-0.051726-0.000000j</td>\n",
       "      <td>-0.052010+0.000000j</td>\n",
       "      <td>-0.052248+0.000000j</td>\n",
       "      <td>-0.052440+0.000000j</td>\n",
       "      <td>-0.052587+0.000000j</td>\n",
       "      <td>-0.052690+0.000000j</td>\n",
       "      <td>-0.052750-0.000000j</td>\n",
       "      <td>-0.052766+0.000000j</td>\n",
       "      <td>-0.052741-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000181-0.000000j</td>\n",
       "      <td>0.000676+0.000000j</td>\n",
       "      <td>0.001048+0.000000j</td>\n",
       "      <td>0.001561-0.000000j</td>\n",
       "      <td>0.002372-0.000000j</td>\n",
       "      <td>0.003530-0.000000j</td>\n",
       "      <td>0.005048-0.000000j</td>\n",
       "      <td>0.006928-0.000000j</td>\n",
       "      <td>0.009163-0.000000j</td>\n",
       "      <td>0.011727-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084583+0.000000j</td>\n",
       "      <td>0.085252+0.000000j</td>\n",
       "      <td>0.085848+0.000000j</td>\n",
       "      <td>0.086372+0.000000j</td>\n",
       "      <td>0.086827+0.000000j</td>\n",
       "      <td>0.087214+0.000000j</td>\n",
       "      <td>0.087535+0.000000j</td>\n",
       "      <td>0.087790+0.000000j</td>\n",
       "      <td>0.087983-0.000000j</td>\n",
       "      <td>0.088113+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>0.000115-0.000000j</td>\n",
       "      <td>0.000427-0.000000j</td>\n",
       "      <td>0.000665-0.000000j</td>\n",
       "      <td>0.000996-0.000000j</td>\n",
       "      <td>0.001512-0.000000j</td>\n",
       "      <td>0.002244-0.000000j</td>\n",
       "      <td>0.003199-0.000000j</td>\n",
       "      <td>0.004381-0.000000j</td>\n",
       "      <td>0.005784-0.000000j</td>\n",
       "      <td>0.007393-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051392-0.000000j</td>\n",
       "      <td>0.051726-0.000000j</td>\n",
       "      <td>0.052010-0.000000j</td>\n",
       "      <td>0.052248-0.000000j</td>\n",
       "      <td>0.052440-0.000000j</td>\n",
       "      <td>0.052587-0.000000j</td>\n",
       "      <td>0.052690+0.000000j</td>\n",
       "      <td>0.052750-0.000000j</td>\n",
       "      <td>0.052766-0.000000j</td>\n",
       "      <td>0.052741-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000115-0.000000j</td>\n",
       "      <td>0.000427+0.000000j</td>\n",
       "      <td>0.000665-0.000000j</td>\n",
       "      <td>0.000996+0.000000j</td>\n",
       "      <td>0.001512+0.000000j</td>\n",
       "      <td>0.002244+0.000000j</td>\n",
       "      <td>0.003199+0.000000j</td>\n",
       "      <td>0.004381-0.000000j</td>\n",
       "      <td>0.005784+0.000000j</td>\n",
       "      <td>0.007393+0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051392+0.000000j</td>\n",
       "      <td>0.051726+0.000000j</td>\n",
       "      <td>0.052010+0.000000j</td>\n",
       "      <td>0.052248+0.000000j</td>\n",
       "      <td>0.052440+0.000000j</td>\n",
       "      <td>0.052587+0.000000j</td>\n",
       "      <td>0.052690+0.000000j</td>\n",
       "      <td>0.052750+0.000000j</td>\n",
       "      <td>0.052766+0.000000j</td>\n",
       "      <td>0.052741+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">10</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000137-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036612+0.000000j</td>\n",
       "      <td>0.039931+0.000000j</td>\n",
       "      <td>0.042889+0.000000j</td>\n",
       "      <td>0.045534-0.000000j</td>\n",
       "      <td>0.047912+0.000000j</td>\n",
       "      <td>0.050071+0.000000j</td>\n",
       "      <td>0.052050+0.000000j</td>\n",
       "      <td>0.053883+0.000000j</td>\n",
       "      <td>0.055598+0.000000j</td>\n",
       "      <td>0.057219+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000134-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008993-0.000000j</td>\n",
       "      <td>-0.009385+0.000000j</td>\n",
       "      <td>-0.009786+0.000000j</td>\n",
       "      <td>-0.010196+0.000000j</td>\n",
       "      <td>-0.010616+0.000000j</td>\n",
       "      <td>-0.011047+0.000000j</td>\n",
       "      <td>-0.011490-0.000000j</td>\n",
       "      <td>-0.011946+0.000000j</td>\n",
       "      <td>-0.012415-0.000000j</td>\n",
       "      <td>-0.012898-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005321+0.000000j</td>\n",
       "      <td>0.005549-0.000000j</td>\n",
       "      <td>0.005782-0.000000j</td>\n",
       "      <td>0.006019-0.000000j</td>\n",
       "      <td>0.006262+0.000000j</td>\n",
       "      <td>0.006512+0.000000j</td>\n",
       "      <td>0.006767+0.000000j</td>\n",
       "      <td>0.007030+0.000000j</td>\n",
       "      <td>0.007299+0.000000j</td>\n",
       "      <td>0.007577+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005321+0.000000j</td>\n",
       "      <td>0.005549-0.000000j</td>\n",
       "      <td>0.005782-0.000000j</td>\n",
       "      <td>0.006019-0.000000j</td>\n",
       "      <td>0.006262+0.000000j</td>\n",
       "      <td>0.006512+0.000000j</td>\n",
       "      <td>0.006767+0.000000j</td>\n",
       "      <td>0.007030+0.000000j</td>\n",
       "      <td>0.007299+0.000000j</td>\n",
       "      <td>0.007577+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000134-0.000000j</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008993+0.000000j</td>\n",
       "      <td>-0.009385+0.000000j</td>\n",
       "      <td>-0.009786-0.000000j</td>\n",
       "      <td>-0.010196+0.000000j</td>\n",
       "      <td>-0.010616+0.000000j</td>\n",
       "      <td>-0.011047-0.000000j</td>\n",
       "      <td>-0.011490+0.000000j</td>\n",
       "      <td>-0.011946+0.000000j</td>\n",
       "      <td>-0.012415+0.000000j</td>\n",
       "      <td>-0.012898+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005321+0.000000j</td>\n",
       "      <td>-0.005549+0.000000j</td>\n",
       "      <td>-0.005782+0.000000j</td>\n",
       "      <td>-0.006019+0.000000j</td>\n",
       "      <td>-0.006262+0.000000j</td>\n",
       "      <td>-0.006512+0.000000j</td>\n",
       "      <td>-0.006767+0.000000j</td>\n",
       "      <td>-0.007030+0.000000j</td>\n",
       "      <td>-0.007299+0.000000j</td>\n",
       "      <td>-0.007577+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005321-0.000000j</td>\n",
       "      <td>-0.005549-0.000000j</td>\n",
       "      <td>-0.005782-0.000000j</td>\n",
       "      <td>-0.006019-0.000000j</td>\n",
       "      <td>-0.006262-0.000000j</td>\n",
       "      <td>-0.006512-0.000000j</td>\n",
       "      <td>-0.006767-0.000000j</td>\n",
       "      <td>-0.007030-0.000000j</td>\n",
       "      <td>-0.007299-0.000000j</td>\n",
       "      <td>-0.007577-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">12</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006051-0.000000j</td>\n",
       "      <td>-0.006437+0.000000j</td>\n",
       "      <td>-0.006788-0.000000j</td>\n",
       "      <td>-0.007113+0.000000j</td>\n",
       "      <td>-0.007416+0.000000j</td>\n",
       "      <td>-0.007706-0.000000j</td>\n",
       "      <td>-0.007985+0.000000j</td>\n",
       "      <td>-0.008259+0.000000j</td>\n",
       "      <td>-0.008530+0.000000j</td>\n",
       "      <td>-0.008801-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">x</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000625+0.000000j</td>\n",
       "      <td>0.000674-0.000000j</td>\n",
       "      <td>0.000726-0.000000j</td>\n",
       "      <td>0.000781-0.000000j</td>\n",
       "      <td>0.000839+0.000000j</td>\n",
       "      <td>0.000901-0.000000j</td>\n",
       "      <td>0.000967+0.000000j</td>\n",
       "      <td>0.001037+0.000000j</td>\n",
       "      <td>0.001111+0.000000j</td>\n",
       "      <td>0.001190+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000362+0.000000j</td>\n",
       "      <td>-0.000390+0.000000j</td>\n",
       "      <td>-0.000419+0.000000j</td>\n",
       "      <td>-0.000451+0.000000j</td>\n",
       "      <td>-0.000484+0.000000j</td>\n",
       "      <td>-0.000520+0.000000j</td>\n",
       "      <td>-0.000558-0.000000j</td>\n",
       "      <td>-0.000598-0.000000j</td>\n",
       "      <td>-0.000640-0.000000j</td>\n",
       "      <td>-0.000686-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000362+0.000000j</td>\n",
       "      <td>-0.000390+0.000000j</td>\n",
       "      <td>-0.000419+0.000000j</td>\n",
       "      <td>-0.000451+0.000000j</td>\n",
       "      <td>-0.000484+0.000000j</td>\n",
       "      <td>-0.000520+0.000000j</td>\n",
       "      <td>-0.000558-0.000000j</td>\n",
       "      <td>-0.000598-0.000000j</td>\n",
       "      <td>-0.000640-0.000000j</td>\n",
       "      <td>-0.000686-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">y</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000625+0.000000j</td>\n",
       "      <td>0.000674+0.000000j</td>\n",
       "      <td>0.000726+0.000000j</td>\n",
       "      <td>0.000781-0.000000j</td>\n",
       "      <td>0.000839+0.000000j</td>\n",
       "      <td>0.000901+0.000000j</td>\n",
       "      <td>0.000967+0.000000j</td>\n",
       "      <td>0.001037+0.000000j</td>\n",
       "      <td>0.001111-0.000000j</td>\n",
       "      <td>0.001190+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000362-0.000000j</td>\n",
       "      <td>0.000390-0.000000j</td>\n",
       "      <td>0.000419-0.000000j</td>\n",
       "      <td>0.000451-0.000000j</td>\n",
       "      <td>0.000484-0.000000j</td>\n",
       "      <td>0.000520+0.000000j</td>\n",
       "      <td>0.000558-0.000000j</td>\n",
       "      <td>0.000598-0.000000j</td>\n",
       "      <td>0.000640-0.000000j</td>\n",
       "      <td>0.000686-0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000362+0.000000j</td>\n",
       "      <td>0.000390+0.000000j</td>\n",
       "      <td>0.000419+0.000000j</td>\n",
       "      <td>0.000451+0.000000j</td>\n",
       "      <td>0.000484+0.000000j</td>\n",
       "      <td>0.000520+0.000000j</td>\n",
       "      <td>0.000558+0.000000j</td>\n",
       "      <td>0.000598+0.000000j</td>\n",
       "      <td>0.000640+0.000000j</td>\n",
       "      <td>0.000686+0.000000j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <th>z</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000422+0.000000j</td>\n",
       "      <td>0.000449+0.000000j</td>\n",
       "      <td>0.000476+0.000000j</td>\n",
       "      <td>0.000501+0.000000j</td>\n",
       "      <td>0.000525+0.000000j</td>\n",
       "      <td>0.000549+0.000000j</td>\n",
       "      <td>0.000573+0.000000j</td>\n",
       "      <td>0.000598+0.000000j</td>\n",
       "      <td>0.000622+0.000000j</td>\n",
       "      <td>0.000648+0.000000j</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Eke                         0.1                 1.1                 2.1   \\\n",
       "L  Labels M                                                                \n",
       "0  z       0  1.000000-0.000000j  1.000000-0.000000j  1.000000-0.000000j   \n",
       "   x       0  1.000000-0.000000j  1.000000-0.000000j  1.000000-0.000000j   \n",
       "   y       0  1.000000-0.000000j  1.000000-0.000000j  1.000000-0.000000j   \n",
       "2  z       0  0.784764-0.000000j  0.292636+0.000000j  0.110286-0.000000j   \n",
       "   x       0 -0.272711-0.000000j -0.638474-0.000000j -0.775003-0.000000j   \n",
       "          -2  0.512102-0.000000j  0.586763+0.000000j  0.614632-0.000000j   \n",
       "           2  0.512102-0.000000j  0.586763+0.000000j  0.614632-0.000000j   \n",
       "   y       0 -0.272711+0.000000j -0.638474-0.000000j -0.775003-0.000000j   \n",
       "          -2 -0.512102+0.000000j -0.586763+0.000000j -0.614632+0.000000j   \n",
       "           2 -0.512102-0.000000j -0.586763-0.000000j -0.614632-0.000000j   \n",
       "4  z       0 -0.040643+0.000000j -0.472493-0.000000j -0.596717-0.000000j   \n",
       "   x       0 -0.059474-0.000000j  0.271217+0.000000j  0.432955-0.000000j   \n",
       "          -2  0.080503-0.000000j -0.153387-0.000000j -0.252803-0.000000j   \n",
       "           2  0.080503-0.000000j -0.153387-0.000000j -0.252803-0.000000j   \n",
       "   y       0 -0.059474-0.000000j  0.271217+0.000000j  0.432955+0.000000j   \n",
       "          -2 -0.080503+0.000000j  0.153387-0.000000j  0.252803-0.000000j   \n",
       "           2 -0.080503+0.000000j  0.153387+0.000000j  0.252803+0.000000j   \n",
       "6  z       0  0.067996+0.000000j  0.124559+0.000000j  0.181056-0.000000j   \n",
       "   x       0 -0.058944+0.000000j -0.107815-0.000000j -0.158159+0.000000j   \n",
       "          -2  0.040201-0.000000j  0.073410+0.000000j  0.107661+0.000000j   \n",
       "           2  0.040201-0.000000j  0.073410+0.000000j  0.107661+0.000000j   \n",
       "   y       0 -0.058944+0.000000j -0.107815-0.000000j -0.158159-0.000000j   \n",
       "          -2 -0.040201+0.000000j -0.073410+0.000000j -0.107661+0.000000j   \n",
       "           2 -0.040201-0.000000j -0.073410-0.000000j -0.107661-0.000000j   \n",
       "8  z       0 -0.000240-0.000000j -0.000812-0.000000j -0.001227-0.000000j   \n",
       "   x       0  0.000181-0.000000j  0.000676+0.000000j  0.001048+0.000000j   \n",
       "          -2 -0.000115-0.000000j -0.000427-0.000000j -0.000665+0.000000j   \n",
       "           2 -0.000115-0.000000j -0.000427-0.000000j -0.000665+0.000000j   \n",
       "   y       0  0.000181-0.000000j  0.000676+0.000000j  0.001048+0.000000j   \n",
       "          -2  0.000115-0.000000j  0.000427-0.000000j  0.000665-0.000000j   \n",
       "           2  0.000115-0.000000j  0.000427+0.000000j  0.000665-0.000000j   \n",
       "10 z       0                 NaN                 NaN                 NaN   \n",
       "   x       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "   y       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "12 z       0                 NaN                 NaN                 NaN   \n",
       "   x       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "   y       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "14 z       0                 NaN                 NaN                 NaN   \n",
       "\n",
       "Eke                         3.1                 4.1                 5.1   \\\n",
       "L  Labels M                                                                \n",
       "0  z       0  1.000000-0.000000j  1.000000-0.000000j  1.000000-0.000000j   \n",
       "   x       0  1.000000-0.000000j  1.000000-0.000000j  1.000000-0.000000j   \n",
       "   y       0  1.000000-0.000000j  1.000000-0.000000j  1.000000-0.000000j   \n",
       "2  z       0  0.047655+0.000000j  0.053351-0.000000j  0.100478-0.000000j   \n",
       "   x       0 -0.814436-0.000000j -0.794373+0.000000j -0.736632+0.000000j   \n",
       "          -2  0.622681-0.000000j  0.618586-0.000000j  0.606800-0.000000j   \n",
       "           2  0.622681-0.000000j  0.618586-0.000000j  0.606800-0.000000j   \n",
       "   y       0 -0.814436-0.000000j -0.794373+0.000000j -0.736632-0.000000j   \n",
       "          -2 -0.622681+0.000000j -0.618586+0.000000j -0.606800+0.000000j   \n",
       "           2 -0.622681-0.000000j -0.618586-0.000000j -0.606800-0.000000j   \n",
       "4  z       0 -0.603036-0.000000j -0.543465+0.000000j -0.445631+0.000000j   \n",
       "   x       0  0.521218-0.000000j  0.562256-0.000000j  0.570921-0.000000j   \n",
       "          -2 -0.294656-0.000000j -0.300540-0.000000j -0.282866+0.000000j   \n",
       "           2 -0.294656-0.000000j -0.300540-0.000000j -0.282866+0.000000j   \n",
       "   y       0  0.521218-0.000000j  0.562256-0.000000j  0.570921-0.000000j   \n",
       "          -2  0.294656-0.000000j  0.300540-0.000000j  0.282866-0.000000j   \n",
       "           2  0.294656+0.000000j  0.300540+0.000000j  0.282866+0.000000j   \n",
       "6  z       0  0.236287+0.000000j  0.289141-0.000000j  0.338700-0.000000j   \n",
       "   x       0 -0.207754-0.000000j -0.255279-0.000000j -0.299612+0.000000j   \n",
       "          -2  0.141347-0.000000j  0.173516-0.000000j  0.203384-0.000000j   \n",
       "           2  0.141347-0.000000j  0.173516-0.000000j  0.203384-0.000000j   \n",
       "   y       0 -0.207754+0.000000j -0.255279-0.000000j -0.299612+0.000000j   \n",
       "          -2 -0.141347+0.000000j -0.173516+0.000000j -0.203384+0.000000j   \n",
       "           2 -0.141347-0.000000j -0.173516-0.000000j -0.203384-0.000000j   \n",
       "8  z       0 -0.001840-0.000000j -0.002813+0.000000j -0.004167-0.000000j   \n",
       "   x       0  0.001561-0.000000j  0.002372-0.000000j  0.003530-0.000000j   \n",
       "          -2 -0.000996-0.000000j -0.001512-0.000000j -0.002244-0.000000j   \n",
       "           2 -0.000996-0.000000j -0.001512-0.000000j -0.002244-0.000000j   \n",
       "   y       0  0.001561-0.000000j  0.002372-0.000000j  0.003530-0.000000j   \n",
       "          -2  0.000996-0.000000j  0.001512-0.000000j  0.002244-0.000000j   \n",
       "           2  0.000996+0.000000j  0.001512+0.000000j  0.002244+0.000000j   \n",
       "10 z       0                 NaN                 NaN                 NaN   \n",
       "   x       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "   y       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "12 z       0                 NaN                 NaN                 NaN   \n",
       "   x       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "   y       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "14 z       0                 NaN                 NaN                 NaN   \n",
       "\n",
       "Eke                         6.1                 7.1                 8.1   \\\n",
       "L  Labels M                                                                \n",
       "0  z       0  1.000000-0.000000j  1.000000-0.000000j  1.000000-0.000000j   \n",
       "   x       0  1.000000-0.000000j  1.000000-0.000000j  1.000000-0.000000j   \n",
       "   y       0  1.000000-0.000000j  1.000000-0.000000j  1.000000-0.000000j   \n",
       "2  z       0  0.172894-0.000000j  0.260212-0.000000j  0.355478-0.000000j   \n",
       "   x       0 -0.655845-0.000000j -0.562321-0.000000j -0.463340-0.000000j   \n",
       "          -2  0.590309-0.000000j  0.571219+0.000000j  0.551014+0.000000j   \n",
       "           2  0.590309-0.000000j  0.571219+0.000000j  0.551014+0.000000j   \n",
       "   y       0 -0.655845-0.000000j -0.562321-0.000000j -0.463340-0.000000j   \n",
       "          -2 -0.590309+0.000000j -0.571219+0.000000j -0.551014+0.000000j   \n",
       "           2 -0.590309-0.000000j -0.571219-0.000000j -0.551014-0.000000j   \n",
       "4  z       0 -0.326262-0.000000j -0.196158-0.000000j -0.062516+0.000000j   \n",
       "   x       0  0.557098-0.000000j  0.527855+0.000000j  0.488402+0.000000j   \n",
       "          -2 -0.249932-0.000000j -0.207619-0.000000j -0.160148-0.000000j   \n",
       "           2 -0.249932-0.000000j -0.207619-0.000000j -0.160148-0.000000j   \n",
       "   y       0  0.557098+0.000000j  0.527855-0.000000j  0.488402-0.000000j   \n",
       "          -2  0.249932-0.000000j  0.207619-0.000000j  0.160148-0.000000j   \n",
       "           2  0.249932+0.000000j  0.207619+0.000000j  0.160148+0.000000j   \n",
       "6  z       0  0.384294-0.000000j  0.425467-0.000000j  0.461963-0.000000j   \n",
       "   x       0 -0.339916-0.000000j -0.375688-0.000000j -0.406740-0.000000j   \n",
       "          -2  0.230377-0.000000j  0.254147+0.000000j  0.274568+0.000000j   \n",
       "           2  0.230377-0.000000j  0.254147+0.000000j  0.274568+0.000000j   \n",
       "   y       0 -0.339916+0.000000j -0.375688-0.000000j -0.406740-0.000000j   \n",
       "          -2 -0.230377-0.000000j -0.254147+0.000000j -0.274568+0.000000j   \n",
       "           2 -0.230377-0.000000j -0.254147-0.000000j -0.274568-0.000000j   \n",
       "8  z       0 -0.005886+0.000000j -0.007949-0.000000j -0.010322-0.000000j   \n",
       "   x       0  0.005048-0.000000j  0.006928+0.000000j  0.009163+0.000000j   \n",
       "          -2 -0.003199-0.000000j -0.004381-0.000000j -0.005784-0.000000j   \n",
       "           2 -0.003199-0.000000j -0.004381-0.000000j -0.005784-0.000000j   \n",
       "   y       0  0.005048-0.000000j  0.006928-0.000000j  0.009163-0.000000j   \n",
       "          -2  0.003199-0.000000j  0.004381-0.000000j  0.005784-0.000000j   \n",
       "           2  0.003199+0.000000j  0.004381-0.000000j  0.005784+0.000000j   \n",
       "10 z       0                 NaN                 NaN                 NaN   \n",
       "   x       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "   y       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "12 z       0                 NaN                 NaN                 NaN   \n",
       "   x       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "   y       0                 NaN                 NaN                 NaN   \n",
       "          -2                 NaN                 NaN                 NaN   \n",
       "           2                 NaN                 NaN                 NaN   \n",
       "14 z       0                 NaN                 NaN                 NaN   \n",
       "\n",
       "Eke                         9.1   ...                41.1                42.1  \\\n",
       "L  Labels M                       ...                                           \n",
       "0  z       0  1.000000-0.000000j  ...  1.000000+0.000000j  1.000000+0.000000j   \n",
       "   x       0  1.000000-0.000000j  ...  1.000000+0.000000j  1.000000+0.000000j   \n",
       "   y       0  1.000000-0.000000j  ...  1.000000+0.000000j  1.000000+0.000000j   \n",
       "2  z       0  0.453965-0.000000j  ...  0.307136+0.000000j  0.353015-0.000000j   \n",
       "   x       0 -0.363902-0.000000j  ...  0.655141+0.000000j  0.650741+0.000000j   \n",
       "          -2  0.530717-0.000000j  ...  0.322705+0.000000j  0.323603+0.000000j   \n",
       "           2  0.530717-0.000000j  ...  0.322705+0.000000j  0.323603+0.000000j   \n",
       "   y       0 -0.363902-0.000000j  ...  0.655141+0.000000j  0.650741-0.000000j   \n",
       "          -2 -0.530717+0.000000j  ... -0.322705+0.000000j -0.323603+0.000000j   \n",
       "           2 -0.530717-0.000000j  ... -0.322705-0.000000j -0.323603-0.000000j   \n",
       "4  z       0  0.069865-0.000000j  ... -0.569503-0.000000j -0.517657+0.000000j   \n",
       "   x       0  0.442583-0.000000j  ... -0.522724+0.000000j -0.532915+0.000000j   \n",
       "          -2 -0.110502-0.000000j  ...  0.555041+0.000000j  0.556708+0.000000j   \n",
       "           2 -0.110502-0.000000j  ...  0.555041+0.000000j  0.556708+0.000000j   \n",
       "   y       0  0.442583-0.000000j  ... -0.522724+0.000000j -0.532915+0.000000j   \n",
       "          -2  0.110502-0.000000j  ... -0.555041+0.000000j -0.556708+0.000000j   \n",
       "           2  0.110502+0.000000j  ... -0.555041-0.000000j -0.556708-0.000000j   \n",
       "6  z       0  0.493736-0.000000j  ... -0.012438-0.000000j -0.026005+0.000000j   \n",
       "   x       0 -0.433136-0.000000j  ... -0.334833+0.000000j -0.323955+0.000000j   \n",
       "          -2  0.291696-0.000000j  ...  0.201422+0.000000j  0.193886+0.000000j   \n",
       "           2  0.291696-0.000000j  ...  0.201422+0.000000j  0.193886+0.000000j   \n",
       "   y       0 -0.433136-0.000000j  ... -0.334833+0.000000j -0.323955+0.000000j   \n",
       "          -2 -0.291696+0.000000j  ... -0.201422+0.000000j -0.193886+0.000000j   \n",
       "           2 -0.291696-0.000000j  ... -0.201422-0.000000j -0.193886-0.000000j   \n",
       "8  z       0 -0.012952-0.000000j  ...  0.016562-0.000000j  0.000498+0.000000j   \n",
       "   x       0  0.011727-0.000000j  ...  0.084583+0.000000j  0.085252-0.000000j   \n",
       "          -2 -0.007393-0.000000j  ... -0.051392-0.000000j -0.051726-0.000000j   \n",
       "           2 -0.007393-0.000000j  ... -0.051392-0.000000j -0.051726-0.000000j   \n",
       "   y       0  0.011727-0.000000j  ...  0.084583+0.000000j  0.085252+0.000000j   \n",
       "          -2  0.007393-0.000000j  ...  0.051392-0.000000j  0.051726-0.000000j   \n",
       "           2  0.007393+0.000000j  ...  0.051392+0.000000j  0.051726+0.000000j   \n",
       "10 z       0  0.000137-0.000000j  ...  0.036612+0.000000j  0.039931+0.000000j   \n",
       "   x       0 -0.000134-0.000000j  ... -0.008993-0.000000j -0.009385+0.000000j   \n",
       "          -2                 NaN  ...  0.005321+0.000000j  0.005549-0.000000j   \n",
       "           2                 NaN  ...  0.005321+0.000000j  0.005549-0.000000j   \n",
       "   y       0 -0.000134-0.000000j  ... -0.008993+0.000000j -0.009385+0.000000j   \n",
       "          -2                 NaN  ... -0.005321+0.000000j -0.005549+0.000000j   \n",
       "           2                 NaN  ... -0.005321-0.000000j -0.005549-0.000000j   \n",
       "12 z       0                 NaN  ... -0.006051-0.000000j -0.006437+0.000000j   \n",
       "   x       0                 NaN  ...  0.000625+0.000000j  0.000674-0.000000j   \n",
       "          -2                 NaN  ... -0.000362+0.000000j -0.000390+0.000000j   \n",
       "           2                 NaN  ... -0.000362+0.000000j -0.000390+0.000000j   \n",
       "   y       0                 NaN  ...  0.000625+0.000000j  0.000674+0.000000j   \n",
       "          -2                 NaN  ...  0.000362-0.000000j  0.000390-0.000000j   \n",
       "           2                 NaN  ...  0.000362+0.000000j  0.000390+0.000000j   \n",
       "14 z       0                 NaN  ...  0.000422+0.000000j  0.000449+0.000000j   \n",
       "\n",
       "Eke                         43.1                44.1                45.1  \\\n",
       "L  Labels M                                                                \n",
       "0  z       0  1.000000+0.000000j  1.000000+0.000000j  1.000000+0.000000j   \n",
       "   x       0  1.000000+0.000000j  1.000000+0.000000j  1.000000+0.000000j   \n",
       "   y       0  1.000000+0.000000j  1.000000+0.000000j  1.000000+0.000000j   \n",
       "2  z       0  0.404836+0.000000j  0.459405-0.000000j  0.514462-0.000000j   \n",
       "   x       0  0.645402-0.000000j  0.639166-0.000000j  0.632075-0.000000j   \n",
       "          -2  0.324693-0.000000j  0.325966-0.000000j  0.327414+0.000000j   \n",
       "           2  0.324693-0.000000j  0.325966-0.000000j  0.327414+0.000000j   \n",
       "   y       0  0.645402+0.000000j  0.639166+0.000000j  0.632075+0.000000j   \n",
       "          -2 -0.324693+0.000000j -0.325966+0.000000j -0.327414+0.000000j   \n",
       "           2 -0.324693-0.000000j -0.325966-0.000000j -0.327414-0.000000j   \n",
       "4  z       0 -0.458173-0.000000j -0.395217-0.000000j -0.331713-0.000000j   \n",
       "   x       0 -0.542405+0.000000j -0.551224+0.000000j -0.559398+0.000000j   \n",
       "          -2  0.557821-0.000000j  0.558406+0.000000j  0.558483+0.000000j   \n",
       "           2  0.557821-0.000000j  0.558406+0.000000j  0.558483+0.000000j   \n",
       "   y       0 -0.542405-0.000000j -0.551224+0.000000j -0.559398+0.000000j   \n",
       "          -2 -0.557821+0.000000j -0.558406+0.000000j -0.558483+0.000000j   \n",
       "           2 -0.557821-0.000000j -0.558406-0.000000j -0.558483-0.000000j   \n",
       "6  z       0 -0.036082-0.000000j -0.043618+0.000000j -0.049363-0.000000j   \n",
       "   x       0 -0.312985+0.000000j -0.301929+0.000000j -0.290790+0.000000j   \n",
       "          -2  0.186317-0.000000j  0.178717-0.000000j  0.171088+0.000000j   \n",
       "           2  0.186317-0.000000j  0.178717+0.000000j  0.171088+0.000000j   \n",
       "   y       0 -0.312985+0.000000j -0.301929+0.000000j -0.290790+0.000000j   \n",
       "          -2 -0.186317+0.000000j -0.178717+0.000000j -0.171088+0.000000j   \n",
       "           2 -0.186317-0.000000j -0.178717-0.000000j -0.171088-0.000000j   \n",
       "8  z       0 -0.014200+0.000000j -0.027442-0.000000j -0.039244+0.000000j   \n",
       "   x       0  0.085848-0.000000j  0.086372-0.000000j  0.086827+0.000000j   \n",
       "          -2 -0.052010+0.000000j -0.052248+0.000000j -0.052440+0.000000j   \n",
       "           2 -0.052010+0.000000j -0.052248+0.000000j -0.052440+0.000000j   \n",
       "   y       0  0.085848+0.000000j  0.086372+0.000000j  0.086827+0.000000j   \n",
       "          -2  0.052010-0.000000j  0.052248-0.000000j  0.052440-0.000000j   \n",
       "           2  0.052010+0.000000j  0.052248+0.000000j  0.052440+0.000000j   \n",
       "10 z       0  0.042889+0.000000j  0.045534-0.000000j  0.047912+0.000000j   \n",
       "   x       0 -0.009786+0.000000j -0.010196+0.000000j -0.010616+0.000000j   \n",
       "          -2  0.005782-0.000000j  0.006019-0.000000j  0.006262+0.000000j   \n",
       "           2  0.005782-0.000000j  0.006019-0.000000j  0.006262+0.000000j   \n",
       "   y       0 -0.009786-0.000000j -0.010196+0.000000j -0.010616+0.000000j   \n",
       "          -2 -0.005782+0.000000j -0.006019+0.000000j -0.006262+0.000000j   \n",
       "           2 -0.005782-0.000000j -0.006019-0.000000j -0.006262-0.000000j   \n",
       "12 z       0 -0.006788-0.000000j -0.007113+0.000000j -0.007416+0.000000j   \n",
       "   x       0  0.000726-0.000000j  0.000781-0.000000j  0.000839+0.000000j   \n",
       "          -2 -0.000419+0.000000j -0.000451+0.000000j -0.000484+0.000000j   \n",
       "           2 -0.000419+0.000000j -0.000451+0.000000j -0.000484+0.000000j   \n",
       "   y       0  0.000726+0.000000j  0.000781-0.000000j  0.000839+0.000000j   \n",
       "          -2  0.000419-0.000000j  0.000451-0.000000j  0.000484-0.000000j   \n",
       "           2  0.000419+0.000000j  0.000451+0.000000j  0.000484+0.000000j   \n",
       "14 z       0  0.000476+0.000000j  0.000501+0.000000j  0.000525+0.000000j   \n",
       "\n",
       "Eke                         46.1                47.1                48.1  \\\n",
       "L  Labels M                                                                \n",
       "0  z       0  1.000000+0.000000j  1.000000+0.000000j  1.000000+0.000000j   \n",
       "   x       0  1.000000+0.000000j  1.000000+0.000000j  1.000000+0.000000j   \n",
       "   y       0  1.000000+0.000000j  1.000000+0.000000j  1.000000+0.000000j   \n",
       "2  z       0  0.568485+0.000000j  0.620505+0.000000j  0.669947-0.000000j   \n",
       "   x       0  0.624163+0.000000j  0.615465+0.000000j  0.606012+0.000000j   \n",
       "          -2  0.329029+0.000000j  0.330804+0.000000j  0.332734+0.000000j   \n",
       "           2  0.329029+0.000000j  0.330804+0.000000j  0.332734+0.000000j   \n",
       "   y       0  0.624163+0.000000j  0.615465+0.000000j  0.606012-0.000000j   \n",
       "          -2 -0.329029+0.000000j -0.330804+0.000000j -0.332734+0.000000j   \n",
       "           2 -0.329029-0.000000j -0.330804-0.000000j -0.332734-0.000000j   \n",
       "4  z       0 -0.269614-0.000000j -0.210149-0.000000j -0.154034-0.000000j   \n",
       "   x       0 -0.566951-0.000000j -0.573903-0.000000j -0.580274-0.000000j   \n",
       "          -2  0.558074+0.000000j  0.557198-0.000000j  0.555870+0.000000j   \n",
       "           2  0.558074+0.000000j  0.557198-0.000000j  0.555870+0.000000j   \n",
       "   y       0 -0.566951-0.000000j -0.573903-0.000000j -0.580274-0.000000j   \n",
       "          -2 -0.558074+0.000000j -0.557198+0.000000j -0.555870+0.000000j   \n",
       "           2 -0.558074-0.000000j -0.557198-0.000000j -0.555870-0.000000j   \n",
       "6  z       0 -0.053889+0.000000j -0.057618-0.000000j -0.060854-0.000000j   \n",
       "   x       0 -0.279573+0.000000j -0.268281+0.000000j -0.256919+0.000000j   \n",
       "          -2  0.163434+0.000000j  0.155756+0.000000j  0.148057+0.000000j   \n",
       "           2  0.163434+0.000000j  0.155756+0.000000j  0.148057+0.000000j   \n",
       "   y       0 -0.279573-0.000000j -0.268281-0.000000j -0.256919+0.000000j   \n",
       "          -2 -0.163434+0.000000j -0.155756+0.000000j -0.148057+0.000000j   \n",
       "           2 -0.163434-0.000000j -0.155756-0.000000j -0.148057-0.000000j   \n",
       "8  z       0 -0.049687+0.000000j -0.058880+0.000000j -0.066945-0.000000j   \n",
       "   x       0  0.087214+0.000000j  0.087535+0.000000j  0.087790+0.000000j   \n",
       "          -2 -0.052587+0.000000j -0.052690+0.000000j -0.052750-0.000000j   \n",
       "           2 -0.052587+0.000000j -0.052690+0.000000j -0.052750-0.000000j   \n",
       "   y       0  0.087214+0.000000j  0.087535+0.000000j  0.087790+0.000000j   \n",
       "          -2  0.052587-0.000000j  0.052690+0.000000j  0.052750-0.000000j   \n",
       "           2  0.052587+0.000000j  0.052690+0.000000j  0.052750+0.000000j   \n",
       "10 z       0  0.050071+0.000000j  0.052050+0.000000j  0.053883+0.000000j   \n",
       "   x       0 -0.011047+0.000000j -0.011490-0.000000j -0.011946+0.000000j   \n",
       "          -2  0.006512+0.000000j  0.006767+0.000000j  0.007030+0.000000j   \n",
       "           2  0.006512+0.000000j  0.006767+0.000000j  0.007030+0.000000j   \n",
       "   y       0 -0.011047-0.000000j -0.011490+0.000000j -0.011946+0.000000j   \n",
       "          -2 -0.006512+0.000000j -0.006767+0.000000j -0.007030+0.000000j   \n",
       "           2 -0.006512-0.000000j -0.006767-0.000000j -0.007030-0.000000j   \n",
       "12 z       0 -0.007706-0.000000j -0.007985+0.000000j -0.008259+0.000000j   \n",
       "   x       0  0.000901-0.000000j  0.000967+0.000000j  0.001037+0.000000j   \n",
       "          -2 -0.000520+0.000000j -0.000558-0.000000j -0.000598-0.000000j   \n",
       "           2 -0.000520+0.000000j -0.000558-0.000000j -0.000598-0.000000j   \n",
       "   y       0  0.000901+0.000000j  0.000967+0.000000j  0.001037+0.000000j   \n",
       "          -2  0.000520+0.000000j  0.000558-0.000000j  0.000598-0.000000j   \n",
       "           2  0.000520+0.000000j  0.000558+0.000000j  0.000598+0.000000j   \n",
       "14 z       0  0.000549+0.000000j  0.000573+0.000000j  0.000598+0.000000j   \n",
       "\n",
       "Eke                         49.1                50.1  \n",
       "L  Labels M                                           \n",
       "0  z       0  1.000000+0.000000j  1.000000+0.000000j  \n",
       "   x       0  1.000000+0.000000j  1.000000+0.000000j  \n",
       "   y       0  1.000000+0.000000j  1.000000+0.000000j  \n",
       "2  z       0  0.716512-0.000000j  0.760083+0.000000j  \n",
       "   x       0  0.595832+0.000000j  0.584952+0.000000j  \n",
       "          -2  0.334812+0.000000j  0.337033+0.000000j  \n",
       "           2  0.334812+0.000000j  0.337033+0.000000j  \n",
       "   y       0  0.595832-0.000000j  0.584952+0.000000j  \n",
       "          -2 -0.334812+0.000000j -0.337033+0.000000j  \n",
       "           2 -0.334812-0.000000j -0.337033-0.000000j  \n",
       "4  z       0 -0.101629-0.000000j -0.053060+0.000000j  \n",
       "   x       0 -0.586083+0.000000j -0.591343+0.000000j  \n",
       "          -2  0.554108+0.000000j  0.551925+0.000000j  \n",
       "           2  0.554108+0.000000j  0.551925+0.000000j  \n",
       "   y       0 -0.586083+0.000000j -0.591343+0.000000j  \n",
       "          -2 -0.554108+0.000000j -0.551925+0.000000j  \n",
       "           2 -0.554108-0.000000j -0.551925-0.000000j  \n",
       "6  z       0 -0.063810-0.000000j -0.066634+0.000000j  \n",
       "   x       0 -0.245491-0.000000j -0.234000+0.000000j  \n",
       "          -2  0.140340-0.000000j  0.132606+0.000000j  \n",
       "           2  0.140340-0.000000j  0.132606+0.000000j  \n",
       "   y       0 -0.245491+0.000000j -0.234000+0.000000j  \n",
       "          -2 -0.140340+0.000000j -0.132606+0.000000j  \n",
       "           2 -0.140340-0.000000j -0.132606-0.000000j  \n",
       "8  z       0 -0.074001+0.000000j -0.080160-0.000000j  \n",
       "   x       0  0.087983+0.000000j  0.088113+0.000000j  \n",
       "          -2 -0.052766+0.000000j -0.052741-0.000000j  \n",
       "           2 -0.052766+0.000000j -0.052741-0.000000j  \n",
       "   y       0  0.087983-0.000000j  0.088113+0.000000j  \n",
       "          -2  0.052766-0.000000j  0.052741-0.000000j  \n",
       "           2  0.052766+0.000000j  0.052741+0.000000j  \n",
       "10 z       0  0.055598+0.000000j  0.057219+0.000000j  \n",
       "   x       0 -0.012415-0.000000j -0.012898-0.000000j  \n",
       "          -2  0.007299+0.000000j  0.007577+0.000000j  \n",
       "           2  0.007299+0.000000j  0.007577+0.000000j  \n",
       "   y       0 -0.012415+0.000000j -0.012898+0.000000j  \n",
       "          -2 -0.007299+0.000000j -0.007577+0.000000j  \n",
       "           2 -0.007299-0.000000j -0.007577-0.000000j  \n",
       "12 z       0 -0.008530+0.000000j -0.008801-0.000000j  \n",
       "   x       0  0.001111+0.000000j  0.001190+0.000000j  \n",
       "          -2 -0.000640-0.000000j -0.000686-0.000000j  \n",
       "           2 -0.000640-0.000000j -0.000686-0.000000j  \n",
       "   y       0  0.001111-0.000000j  0.001190+0.000000j  \n",
       "          -2  0.000640-0.000000j  0.000686-0.000000j  \n",
       "           2  0.000640+0.000000j  0.000686+0.000000j  \n",
       "14 z       0  0.000622+0.000000j  0.000648+0.000000j  \n",
       "\n",
       "[46 rows x 51 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabulate complex values...\n",
    "mTermSTpd, _ = ep.util.multiDimXrToPD(mTermST, colDims='Eke', thres = thres)\n",
    "mTermSTpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mTermST.XS.real.squeeze().plot.line(x='Eke', col='Labels');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verification - test vs. existing calcs\n",
    "\n",
    "The [existing method](https://epsproc.readthedocs.io/en/dev/demos/ePSproc_BLM_calc_demo_Sept2019.html), implemented in `mfblm()`, makes use of a set of nested loops; this is far from optimal and rather slow (although could be sped up with parallel methods)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### N2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time = 24.903498649597168 seconds, for 51 energy points, single pol geom, thres=0.0001.\n"
     ]
    }
   ],
   "source": [
    "daIn = dataSet[0].copy()\n",
    "\n",
    "# BLMXeN2 = ep.mfblm(daIn[:, 1:4], selDims = {'Type':'L'}, thres = 1e-4)       # Subselected on Eke\n",
    "start = time.time()\n",
    "BLMXeN2 = ep.mfblm(daIn, selDims = selDims, thres = thres, verbose = False)   # Run for all Eke. Note this fails if selDims = None, to fix!\n",
    "end = time.time()\n",
    "print('Elapsed time = {0} seconds, for {1} energy points, single pol geom, thres={2}.'.format((end-start), BLMXeN2.Eke.size, thres))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time = 74.48245048522949 seconds, for 51 energy points, 3 polarisations, threshold=0.0001.\n"
     ]
    }
   ],
   "source": [
    "# Set standard pol geoms (== (z,x,y) as used above)\n",
    "eAngs = ep.setPolGeoms()\n",
    "\n",
    "# Calculate for each pol geom\n",
    "start = time.time()\n",
    "BLM = ep.mfblmEuler(dataSet[0], selDims=selDims, eAngs = eAngs, thres = thres, SFflag = True, verbose = 0)  # Run for all Eke.  Note this fails if selDims = None, to fix!\n",
    "end = time.time()\n",
    "print('Elapsed time = {0} seconds, for {1} energy points, {2} polarisations, threshold={3}.'.format((end-start), BLM.Eke.size, eAngs.size, thres))\n",
    "\n",
    "# Elapsed time = 35.40217471122742 seconds, for 51 energy points, 3 polarisations, threshold=0.01.\n",
    "# Elapsed time = 77.12336850166321 seconds, for 51 energy points, 3 polarisations, threshold=0.0001."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting data n2_3sg_0.1-50.1eV_A2.inp.out, pType=a, thres=0.01, with Seaborn\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daPlot, daPlotpd, legendList, gFig = ep.lmPlot(BLM.swap_dims({'Euler':'Labels'}), SFflag = False, eulerGroup = True, fillna = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "BLMrename = BLM.swap_dims({'Euler':'Labels'}).unstack('BLM').rename({'l':'L','m':'M'}).stack({'LM':['L','M']})  # Reformat Xarray\n",
    "testSub = mTermST - BLMrename\n",
    "testSub.attrs['dataType']='matE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min difference: (-0.01001260432521213-1.9073640679125083e-17j), max difference: (0.02883058068349678+1.7578510363291103e-17j), threshold: 0.0001\n"
     ]
    }
   ],
   "source": [
    "print(f\"Min difference: {testSub.min().data}, max difference: {testSub.max().data}, threshold: {thres}\")\n",
    "\n",
    "# For thres = 1e-4, max diff = 0.0289\n",
    "# For thres = 1e-2, max diff = 0.3781"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: Difference is >theshold here, so need to check differences in more detail. Due to difference in thresholding and accumulation in fns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting data (No filename), pType=a, thres=None, with Seaborn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daPlot, daPlotpd, legendList, gFig = ep.lmPlot(testSub, xDim='Eke', SFflag = False, fillna = True, thres = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min difference: -3.3306690738754696e-16, max difference: 3.3306690738754696e-16\n"
     ]
    }
   ],
   "source": [
    "# Check pol geoms as before\n",
    "testSubXY = mTermST.sel({'Labels':'x'}).pipe(np.abs) - mTermST.sel({'Labels':'y'}).pipe(np.abs)\n",
    "\n",
    "print(f\"Min difference: {testSubXY.min().data}, max difference: {testSubXY.max().data}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalize and plot results - Original mfblm() results vs. geomFunc case\n",
    "BLM.XS.real.squeeze().plot.line(x='Eke', col='Euler');\n",
    "# mTermST.XS.sum(['Cont', 'Total']).real.squeeze().plot.line(x='Eke', col='Labels');\n",
    "mTermST.XS.real.squeeze().plot.line(x='Eke', col='Labels');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1092.12x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1092.12x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JVKvVgre3txAUFNQmYxJR2+jq89kvRUdHCwCE77//vs3Hoo6Lm6p2PE/6nkgEgS/IJiIiIiIiaq5NmzZh//79uHz5stipkIhqamqQn5+Pvn37NrnHDbW/J31PuKkqERERERFRM9y7dw9paWnYvn073njjDbHTIWqRsrIy2NvbQ6FQtPvY48aNw+HDh9t9XBZEiIiIiIiImmHFihX4zW9+g0mTJvHtMvTUi4yMxMyZM3VeG52WloapU6eiR48esLGxQUBAQKN9g57kH//4ByZOnAgbGxvY2Njgueeew/fff6/TJjw8HGvXroVGo2mNW9EbCyJERERERETNEBMTg9raWhw4cADGxsZip0PUbCqVCtHR0Vi6dKk2dvfuXcjlcri5ueHcuXM4ffo0rKysIJfLG70++3ESExMxf/58nDx5EqmpqXBzc0NAQACKioq0bWbMmIHKykocO3asVe/rSbiHCBEREREREVELPc17iBw+fBivvfYabt++rY2dP38e48aNQ0FBAVxdXQEAly9fxsiRI/HDDz+gf//+zRqroaEBNjY2+Mtf/oKFCxdq44sXL0ZDQwM+//zzlt3ML3APESIiIiIiIiIxCAJQd1+cw4C1D0lJSfDy8tKJDR48GLa2toiOjkZdXZ12Fcnw4cPRp0+fZn9JqquroVar0bNnT524t7c3kpOTm91vc0jbdTQiIiIiIiKirkJdDUQ4izP2u8WAqYVeTRUKBZyddfO0tLREYmIiZs+ejQ8++AAAMGjQIBw7dgxSafNLCWvXroWLiwuee+45nbiLiwsKCgqg0WhgZNQ+azfapSCyY8cOfPTRR7h16xaGDx+OqKgoTJw48ZHtDx06hPDwcPz444/o378/Nm3ahBdffFF7/v3338f+/ftRWFgIU1NTjB07Fps2bYKPj49e+Wg0GhQXF8PS0hISiaTF90dEpC9BEHD37l04Ozu3ykTP+YyIxML5jIg6i9aez55GKpWq0SMlKpUKS5YswYQJExAbG4uGhgZ8/PHHmD59OtLS0mBubm7wOH/+858RGxuLxMTERuOZm5tDo9Ggtra2WX03i9DG9u/fL5iYmAj/+Mc/hJycHCE0NFSwsLAQbty40WT7lJQUwdjYWIiIiBCuXLkiRERECFKpVDh79qy2zb59+4SEhAThxx9/FLKysoSXX35ZsLKyEkpKSvTKqbCwUADAgwcPHqIdhYWFrTLHcj7jwYOH2AfnMx48eHSWo6XzmUqlEnJycgSVSvVzUKMRhNp74hwajd65L1iwQJg/f75O7J///Kdgb28vNDQ0aGO1tbVCt27dhNjYWIO/Ph999JFgbW0tpKWlNXl+//79Qrdu3Qzu93Ga/J78QpuvENmyZQtefvll7W61UVFROHbsGHbu3InIyMhG7aOiouDv74+wsDAAQFhYGE6dOoWoqCjExsYCABYsWNBojOjoaGRmZmLq1KlPzMnS0hIAUFhYCCsrqxbdHxGRIaqqquDq6qqdh1qK8xkRiYXzGRF1Fq09n+mQSPR+bEVMnp6e2Lt3r06suroaRkZGOqv2Hn429PW4H330Ef70pz/h2LFjjfYqeSgrKwtjxowxPPkWaNOCSF1dHdLT07F27VqdeEBAAFJSUpq8JjU1FW+99ZZOTC6XIyoq6pFj7Nq1C9bW1hg1alSTbWpra1FbW6v9fPfuXQCAlZUV/8ElIlE0dzk45zMi6mg4nxFRZ9GVH9eTy+UICwtDRUUFbGxsAAD+/v5Ys2YNXn/9daxcuRIajQYffvghpFIppkyZonfff/7znxEeHo4vvvgC7u7uUCqVAIDu3buje/fu2nbJyckICAho3Rt7gjZ9QKq0tBQNDQ1wcHDQiTs4OGi/CL+mVCr1av+f//wH3bt3h5mZGT799FMkJCTA1ta2yT4jIyNhbW2tPR6+MoiI6GnD+YyIOgvOZ0REHYeHhwe8vLxw8OBBbWzIkCH4+uuvkZmZCV9fX0ycOBHFxcWIj4+Hk5OTtp1EIkFMTMwj+96xYwfq6uoQGBgIJycn7fHxxx9r2xQVFSElJQWLFy9uk/t7lHbZMebXlTZBEB5bfdOn/ZQpU5CRkYGUlBRMmzYNQUFBKCkpabK/sLAwVFZWao/CwsJm3gkRkbg4nxFRZ8H5jIioYwkPD8fWrVt1Hofx9/fH6dOncefOHZSXl+O///0vxo8frz2vUCgglUoxYcKER/arUCggCEKj4/3339e2iYqKQkhICHr37t0m9/YobfrIjK2tLYyNjRut7igpKWm0CuQhR0dHvdpbWFhgwIABGDBgAMaPH4+BAwciOjpau/fIL8lkMshkshbeDRGR+DifEVFnwfmMiKhjmT59OvLy8lBUVKT3qr34+Hi8+uqrGDhwYIvGtre3x+rVq1vUR3O06QqRh6/ETUhI0IknJCTAz8+vyWt8fX0btT9+/Pgj2z8kCILOc6hEREREREREpL/Q0FCDHmFctmwZ/vrXv7Z43DVr1jxy0URbavO3zKxatQrBwcHw8vKCr68vdu3ahYKCAixbtgwAsHDhQri4uGjfOBMaGopnnnkGmzdvxuzZs/HVV1/hxIkTOH36NADg/v372LRpE2bNmgUnJyeUlZVhx44duHnzJn7/+9+39e0QEWkJgoC75TWw6tVO70knIiIiIqJW0+YFkblz56KsrAwbN27ErVu3MGLECHz77bfo06cPAKCgoABGRj8vVPHz88P+/fuxfv16hIeHo3///jhw4AB8fHwAAMbGxrh69Sr27NmD0tJS9OrVC+PGjUNycjKGDx/e1rdDRKR19qvryDpVhOdfG4HeQ3qKnQ4RERERERmgzQsiALB8+XIsX768yXOJiYmNYoGBgQgMDGyyvZmZGQ4fPtya6RERGSwrqQgX4m8AAO6W14icDRERERERGapd3jJDRNSZKDJLkRSbCwAYN8MdQ/2cRc6IiIiIiIgMxYIIEZEBflJU4dg/syAIgM0oCbYbbUB5TbnYaRERERERkYFYECEi0lPlbRW++esl1NdpYDPABFu7hyGjNAN7sveInRoRERERERmIBREiIj3U3FPjP3+5BNVdNaycTfEPx/dRrbmPCc4TsMJzhdjpERERERGRgVgQISJ6gga1Bt/+LRN3fqpGNxsTfNn/U5Q2lGB4r+HYMnkLTIxMxE6RiIiIiKhFysrKYG9vD4VC0e5jjxs3TpSXp7AgQkT0GIIg4NT+XNz6oRIm5sY4OeJz5Ndfg5ulG/469a/oZtJN7BSJiIiIiFosMjISM2fOhLu7uzaWlpaGqVOnokePHrCxsUFAQAAyMjIM7vvQoUMYNmwYZDIZhg0bhri4OJ3z4eHhWLt2LTQaTUtvwyAsiBARPcblxJu4cuYWJBIge/QxXKw/i15mvfA3/7+hl3kvsdMjIiIiImoxlUqF6OhoLF26VBu7e/cu5HI53NzccO7cOZw+fRpWVlaQy+VQq9V6952amoq5c+ciODgYly5dQnBwMIKCgnDu3DltmxkzZqCyshLHjh1r1ft6EhZEiIgeofBqOU5/+QMAoHRUDr7D17AwscDO53bC1dJV5OyIiIiIiFrH0aNHIZVK4evrq43l5uaioqICGzduxODBgzF8+HBs2LABJSUlKCgo0LvvqKgo+Pv7IywsDEOGDEFYWBimTp2KqKgobRtjY2NMnz4dsbGxrXpfT8KCCBFREypvV+PYriwIGgGagRX40uzvkBpJsXXKVgztNVTs9IiIiIjoKSAIAqrV1aIcgiDonWdSUhK8vLx0YoMHD4atrS2io6NRV1enXUUyfPhw9OnTR+++U1NTERAQoBOTy+VISUnRiXl7eyM5OVnvfluDtF1HIyJ6CtSp6vHNjsuora6HqVMD/trzT4AE+KPfH+Hj5CN2ekRERET0lFDVq+DzhTg/P55bcE7v/e4UCgWcnZ11YpaWlkhMTMTs2bPxwQcfAAAGDRqEY8eOQSrVv5SgVCrh4OCgE3NwcIBSqdSJubi4oKCgABqNBkZG7bN2gytEiIh+QdAISNidg4pb92FiKcEel01oMKrHyyNexqz+s8ROj4iIiIio1alUKpiZmTWKLVmyBBMmTMDZs2dx5swZDB8+HNOnT4dKpTKof4lEovNZEIRGMXNzc2g0GtTW1jbvJpqBK0SIiH4h/dgNKDJLYSSV4OigXag0KcNUt6l4Y8wbYqdGRERERE8Zc6k5zi049+SGbTS2vmxtbVFRUaET++KLL6BQKJCamqpdsfHFF1/AxsYGX331FebNm6dX346Ojo1Wg5SUlDRaNVJeXo5u3brB3Fz/vFuKBREiov9TlFuB749cBwBkDUnAD6ZZGNpzKCJ+EwEjCRfUEREREZFhJBKJ3o+tiMnT0xN79+7ViVVXV8PIyEhnJcfDz4a8HtfX1xcJCQl46623tLHjx4/Dz89Pp11WVhbGjBnTzDtoHv6ET0QE4H5lLY5HZ0MQgDvuCiRZfg07cztse3bbU/GPGBERERFRc8nlcmRnZ+usEvH390dFRQVef/11XLlyBdnZ2Vi8eDGkUimmTJmid9+hoaE4fvw4Nm/ejKtXr2Lz5s04ceIE3nzzTZ12ycnJjTZfbWssiBBRl6fRCEj4LBvVVXUQetbgX/Z/gcxYhm3PboOjhaPY6RERERERtSkPDw94eXnh4MGD2tiQIUPw9ddfIzMzE76+vpg4cSKKi4sRHx8PJycnbTuJRIKYmJhH9u3n54f9+/dj9+7dGDlyJGJiYnDgwAH4+Py82WxRURFSUlKwePHiNrm/R+EjM0TU5aX9Jx9FuXdgZAJ84fYJ6o3V+MAvEiNsR4idGhERERFRuwgPD8fq1avxyiuvaPcM8ff3h7+//yOvUSgUkEqlmDBhwmP7DgwMRGBg4CPPR0VFISQkBL17925e8s3EgggRdWkF2WU4f1QBAEjufxB3zEswb/A8vNDvBXETIyIiIiJqR9OnT0deXh6Kiorg6uqq1zXx8fF49dVXMXDgwBaNbW9vj9WrV7eoj+ZgQYSIuqx7FbVI2J0DCECxWzYu25zBSNuReHvc22KnRkRERETU7kJDQw1qv2zZslYZd82aNa3Sj6G4hwgRdUmCRsCJmBzU3FND3fMuvnH6DDYyG3wy+ROYGJuInR4REREREbUxFkSIqEu69F0hinIrIJEK+JfbVgjGGvx50p+5iSoRERERURfBgggRdTllRfdw9t/XAQCn3Q6j0vw2VnquxHin8SJnRkRERERE7YUFESLqUhrUGiTszkFDvQYldtdx2T4Jk3tPxpIRS8ROjYiIiIiI2hELIkTUpZz7+jrKbt6DRqbGUbfPYG9hjw8mfAAjCadDIiIiIqKuhG+ZIaIuo+haBS4mFAAAjrvvQY3pPWz/zafoYdZD5MyIiIiIiKi98U+iRNQl1KrqcSLmwSt2f3RMh6LnZYSMCIGPk4/YqRERERERkQhYECGiLuH0l3m4V16LWou7SHQ7gKE9h2Ll6JVip0VERERE1CGUlZXB3t4eCoVC7FRQUlICOzs7FBUVtek4LIgQUadXeKUcV1NuARBw1P0zSGVG2PzMZpgYm4idGhERERFRhxAZGYmZM2fC3d0dAHDp0iXMnz8frq6uMDc3x9ChQ7F169YWj6NWq/HOO+/Aw8MDFhYWcHZ2xsKFC1FcXKxtY29vj+DgYGzYsKHF4z0OCyJE1Kmp6xqQuO8qACDb6QyUVtfx9ri30de6r8iZERERERF1DCqVCtHR0Vi6dKk2lp6eDjs7O+zduxfZ2dlYt24dwsLC8Je//KVFY1VXV+PChQsIDw/HhQsXcPjwYVy7dg2zZs3Sabd48WLs27cPFRUVLRrvcbipKhF1amlf56OqtAY1Zndx1vUIprpNxe8G/k7stIiIiIiIOoyjR49CKpXC19dXG1uyZIlOm379+iE1NRWHDx/GihUrmj2WtbU1EhISdGLbt2+Ht7c3CgoK4ObmBgDw8PCAo6Mj4uLiGuXSWlgQIaJO63bBXWScePBWme/cY2FpYYENvhsgkUhEzoyIiIiIugJBECCoVKKMLTE31/vn3qSkJHh5eT2xXWVlJXr27NnS1JrsVyKRoEcP3bc/ent7Izk5mQURIiJDNDRo8N3/XoEgAD/aXkSBTTY+8vkINpvaQFsAACAASURBVGY2YqdGRERERF2EoFIhd8xYUcYefCEdkm7d9GqrUCjg7Oz82Dapqak4ePAgvvnmm9ZIT6umpgZr167FggULYGVlpXPOxcUFFy9ebNXxfol7iBBRp3TpRCFKC+9BbVKD030OYarbVMj7yMVOi4iIiIiow1GpVDAzM3vk+ezsbMyePRvvvfce/P399e5337596N69u/ZITk7WOa9WqzFv3jxoNBrs2LGj0fXm5uaorq7W/0YMxBUiRNTp3Cmpxvf/yQcAnHY7BGl3YJ3POj4qQ0RERETtSmJujsEX0kUbW1+2traP3Lw0JycHzz77LF555RWsX7/eoBxmzZoFHx8f7WcXFxftf6vVagQFBSE/Px/fffddo9UhAFBeXg47OzuDxjQECyJE1KkIgoBTX+SiQa1BsXUecu2+xwfjPoBdt7abSImIiIiImiKRSPR+bEVMnp6e2Lt3b6N4dnY2nn32WSxatAibNm0yuF9LS0tYWlo2ij8shuTl5eHkyZPo1atXk9dnZWVh8uTJBo+rLz4yQ0Sdyo8XbuPm1QpojOqR2C8WE1wmYHb/2WKnRURERETUYcnlcmRnZ+usEsnOzsaUKVPg7++PVatWQalUQqlU4vbt2y0aq76+HoGBgTh//jz27duHhoYGbd91dXXadtXV1UhPT0dAQECLxnscFkSIqNNQ1zXgzKE8AMAF5wTUd1fxrTJERERERE/g4eEBLy8vHDx4UBv78ssvcfv2bezbtw9OTk7aY9y4cdo2CoUCEokEiYmJeo918+ZNHDlyBDdv3sTo0aN1+k5JSdG2++qrr+Dm5oaJEye2yj02hQURIuo0Lh67gXvltbgnq0CG83/x1ti34NTdSey0iIiIiIg6vPDwcGzduhUajQYA8P777z94bfCvDoVCob1GoVCgR48eGDVqlN7juLu7N9mvIAg6j8d8+umneO+991rr9prEPUSIqFOoKlPhwvECAEBKnzgMcxyKoMFBImdFRERERPR0mD59OvLy8lBUVARXV1e9romPj8e7774LGxubVs2lpKQEgYGBmD9/fqv2+2vtskJkx44d6Nu3L8zMzDB27NhGr9r5tUOHDmHYsGGQyWQYNmwY4uLitOfUajXeeecdeHh4wMLCAs7Ozli4cCGKi4vb+jaIqANLOfQDGtQaFFnlIb9nJtb5rIORhIvgiIiIiIj0FRoaqncxBAA+/PBDrFmzptXzsLe3x9tvv93mj763+W8LBw4cwJtvvol169bh4sWLmDhxIp5//nkUFBQ02T41NRVz585FcHAwLl26hODgYAQFBeHcuXMAHmyscuHCBYSHh+PChQs4fPgwrl27hlmzZrX1rRBRB3Xzajl+vHAbAjQ4434IQUOCMKzXMLHTIiIiIiKiDkwiCILQlgP4+PhgzJgx2LlzpzY2dOhQzJkzB5GRkY3az507F1VVVTh69Kg2Nm3aNNjY2CA2NrbJMdLS0uDt7Y0bN27Azc3tiTlVVVXB2toalZWVTb7rmIieHpoGDQ5sSkN58X1cdkxC9pD/4j8v/gfWMmuxU2tSa88/nM+ISCycz4ios2it+aempgb5+fnapyNIfE/6nrTpCpG6uromX5MTEBCgs3vsL6WmpjZqL5fLH9keACorKyGRSNCjR48mz9fW1qKqqkrnIKLOISupCOXF91EjvY/zvY8idExohy2GtAbOZ0TUWXA+IyIisbVpQaS0tBQNDQ1wcHDQiTs4OECpVDZ5jVKpNKh9TU0N1q5diwULFjyymhcZGQlra2vtYcgzUUTUcdXcU+P7r/MBAN+7foOBjv3w24G/FTmrtsX5jIg6C85nREQktnbZcfDXG6EIgvDYzVH0ba9WqzFv3jxoNBrs2LHjkf2FhYWhsrJSexQWFhp4B0TUEaXHK1BbXY/SbkW46pCKd33e7fQbqXI+I6LOgvMZERGJrU1fu2trawtjY+NGqztKSkoarQJ5yNHRUa/2arUaQUFByM/Px3fffffYZ71kMhlkMlkz74KIOqK75TW4nHgTAHDO7QheHPQiPOw8RM6q7XE+I6LOgvMZERGJrU3/lGpqaoqxY8ciISFBJ56QkAA/P78mr/H19W3U/vjx4zrtHxZD8vLycOLECfTq1av1kyeiDi3tP/loqBdQbJWHO/ZFeGPMG2KnRERERERET5E2X1u+atUq/POf/8Rnn32GK1eu4K233kJBQQGWLVsGAFi4cCHCwsK07UNDQ3H8+HFs3rwZV69exebNm3HixAm8+eabAID6+noEBgbi/Pnz2LdvHxoaGqBUKqFUKlFXV9fWt0NEHUB58X1cTb0FADjr9jVeG/Uaepr1FDkrIiIiIqKnV1lZGezt7aFQKNp97HHjxuHw4cPtPm6bF0Tmzp2LqKgobNy4EaNHj0ZSUhK+/fZb9OnTBwBQUFCAW7duadv7+flh//792L17N0aOHImYmBgcOHAAPj4+AICbN2/iyJEjuHnzJkaPHg0nJyft8bg30RBR53HuyHUIApBvkwmpoxrzh8wXOyUiIiIioqdaZGQkZs6cCXd3d514TEwMRo4cCTMzMzg6OmLFihUG933o0CEMGzYMMpkMw4YNQ1xcnM758PBwrF27FhqNpiW3YLA23UPkoeXLl2P58uVNnktMTGwUCwwMRGBgYJPt3d3dIQhCa6ZHRE8R5fVKXM+4DQ00+N7tP1jjGQpTY1Ox0yIiIiIiemqpVCpER0fj22+/1Ylv2bIFn3zyCT766CP4+PigpqYG169fN6jv1NRUzJ07Fx988AFefPFFxMXFISgoCKdPn9YufJgxYwZeeeUVHDt2DM8//3yr3deTtEtBhIioNQiCgNS4HwEA1+y+h0NvG8zoN0PkrIiIiIiInm5Hjx6FVCqFr6+vNlZRUYH169fj66+/xtSpU7Xx4cOHG9R3VFQU/P39tVtlhIWF4dSpU4iKikJsbCwAwNjYGNOnT0dsbGy7FkQ69/spiahTKcgpR3HeHdRL1DjvGo+3xrzV6V+zS0RERERPL0EQoK5tEOUw5MmKpKQkeHl56cQSEhKg0WhQVFSEoUOHonfv3ggKCjL4NempqakICAjQicnl8kZbXnh7eyM5OdmgvluKK0SI6KkgaASc/feD1SHZjqcxwn0w/FyaflsVEREREVFHUF+nwa7QU6KM/erWSTCRGevVVqFQwNnZWSd2/fp1aDQaREREYOvWrbC2tsb69evh7++PzMxMmJrq99i6UqmEg4ODTszBwQFKpVIn5uLigoKCAmg0GhgZtc8fPVkQIaKnwg8XSlBaeA+1xipcdEnA52N3i50SEREREVGnoFKpYGZmphPTaDRQq9XYtm2bdoVHbGwsHB0dcfLkScjlcr37l0gkOp8FQWgUMzc3h0ajQW1tLczNzZt5J4ZhQYSIOjxBI+D8NwoAQKZTIqYOnoyhvYaKmxQRERER0RNITY3w6tZJoo2tL1tbW1RUVOjEnJycAADDhg3Txuzs7GBra4uCggK9+3Z0dGy0GqSkpKTRqpHy8nJ069at3YohAPcQIaKnQP6lUpTfuo9aYxWuupzBSs+VYqdERERERPREEokEJjJjUY5fr8B4HE9PT+Tk5OjEJkyYAADIzc3VxsrLy1FaWoo+ffro3bevry8SEhJ0YsePH4efn+7j71lZWRgzZoze/bYGFkSIqEMTBAFp3+YDALIdk/Hi8Nlw6e4iclZERERERJ2HXC5Hdna2ziqRQYMGYfbs2QgNDUVKSgqysrKwaNEiDBkyBFOmTNG779DQUBw/fhybN2/G1atXsXnzZpw4cQJvvvmmTrvk5ORGm6+2NRZEiKhDu5FVhtLCe1Ab1eJq7xS87PGy2CkREREREXUqHh4e8PLywsGDB3Xin3/+OXx8fDBjxgxMmjQJJiYmiI+Ph4mJibaNRCJBTEzMI/v28/PD/v37sXv3bowcORIxMTE4cOAAfHx8tG2KioqQkpKCxYsXt/q9PQ73ECGiDksQBJz/VgEAyHY4jVnDZ8DW3FbcpIiIiIiIOqHw8HCsXr0ar7zyivYtL1ZWVoiOjkZ0dHST1ygUCkilUu3jNY8SGBiIwMDAR56PiopCSEgIevfu3fwbaAYWRIiow7qZW4Gf8qtQL6nDld5nsGnEwSdfREREREREBps+fTry8vJQVFQEV1dXva6Jj4/Hq6++ioEDB7ZobHt7e6xevbpFfTQHCyJE1GE9fLPMFYdUzBghh303e3ETIiIiIiLqxEJDQw1qv2zZslYZd82aNa3Sj6G4hwgRdUjFeXdQnHcHDZJ6ZPdOwuIR7fs8IRERERERdW4siBBRh3T+qAIAkGt3DvIRU+Fo4ShuQkRERERE1KmwIEJEHc5P+VUozCmHBg247JqIpR5LxU6JiIiIiIg6GRZEiKjDSY9XAADy7M7Df8Rkrg4hIiIiIqJWx01ViahDuVNSjfzMUgDA5d6J+MIjRtyEiIiIiIioU+IKESLqUDK/uwkIwI0e2Zg80g9O3Z3ETomIiIiIiDohFkSIqMOoua9G9pkiAMBl51NYMnyJyBkREREREVFnxYIIEXUYOaeLoVELKO1WhKEebnC1chU7JSIiIiKiLqGsrAz29vZQKBRip9JISUkJ7OzsUFRU1Kr9siBCRB1CQ70GF/97AwCQ6XQSISNCxE2IiIiIiKgLiYyMxMyZM+Hu7q6NhYaGYuzYsZDJZBg9enST112+fBmTJk2Cubk5XFxcsHHjRgiCoPe4arUa77zzDjw8PGBhYQFnZ2csXLgQxcXF2jb29vYIDg7Ghg0bmn1/TWFBhIg6hB/SS1BTVY/7JpWwHCpgpN1IsVMiIiIiIuoSVCoVoqOjsXTpUp24IAhYsmQJ5s6d2+R1VVVV8Pf3h7OzM9LS0rB9+3Z8/PHH2LJli95jV1dX48KFCwgPD8eFCxdw+PBhXLt2DbNmzdJpt3jxYuzbtw8VFRWG3+Aj8C0zRCQ6QRBw8YQCAJDtmIyQkQvFTYiIiIiIqAs5evQopFIpfH19deLbtm0DANy+fRuZmZmNrtu3bx9qamoQExMDmUyGESNG4Nq1a9iyZQtWrVoFiUTyxLGtra2RkJCgE9u+fTu8vb1RUFAANzc3AICHhwccHR0RFxeHJUtaZ69BrhAhItEV591BWWE11EZ1uDugEJNcJ4mdEhERERFRiwmCAHVNjSiHIY+tJCUlwcvLy+D7S01NxaRJkyCTybQxuVyO4uLiFu1FUllZCYlEgh49eujEvb29kZyc3Ox+f40rRIhIdBdPFAAArtl9j/meQTCSsFZLRERERE+/+tpabFsUKMrYb+z5F0zMzPRqq1Ao4OzsbPAYSqVSZ88RAHBwcNCe69u3r8F91tTUYO3atViwYAGsrKx0zrm4uODixYsG9/ko/K2DiER156dq3MgsBQDccL+Imf1mipwREREREVHXolKpYKZn8eTXfv1YzMOVKfo8LvNrarUa8+bNg0ajwY4dOxqdNzc3R3V1dbPybApXiBCRqC79txCABAqby3hhTADMpM2biImIiIiIOhqpTIY39vxLtLH1ZWtr26zNSh0dHaFUKnViJSUlAH5eKaIvtVqNoKAg5Ofn47vvvmu0OgQAysvLYWdnZ3Cej8KCCBGJplZVj5zUB+8Sv+qSgncH/03kjIiIiIiIWo9EItH7sRUxeXp6Yu/evQZf5+vri3fffRd1dXUwNTUFABw/fhzOzs6NHqV5nIfFkLy8PJw8eRK9evVqsl1WVhYmT55scJ6PwkdmiEg0uWdvQaMGys1vwWesB2zMbMROiYiIiIioy5HL5cjOzm60SuSHH35ARkYGlEolVCoVMjIykJGRgbq6OgDAggULIJPJEBISgqysLMTFxSEiIkLvN8wAQH19PQIDA3H+/Hns27cPDQ0NUCqVUCqV2nGAB6/nTU9PR0BAQKvdNwsiRCQKQRBw8aQCAJDteBrBw4PFTYiIiIiIqIvy8PCAl5cXDh48qBNfunQpPD098fe//x3Xrl2Dp6cnPD09UVxcDODnV+bevHkTXl5eWL58OVatWoVVq1Zp+0hMTIREInnkW2du3ryJI0eO4ObNmxg9ejScnJy0R0pKirbdV199BTc3N0ycOLHV7puPzBCRKIqv3cG9EjXURrWwHWmCPlZ9xE6JiIiIiKjLCg8Px+rVq/HKK6/AyOjB2onExMQnXufh4YGkpKRHnlcoFBgwYABcXFyaPO/u7q7XK4I//fRTvPfee09sZwgWRIhIFBmJD1+1ex7zPMR5FRkRERERET0wffp05OXloaioCK6urq3Wb3x8PCIiImBiYtLsPkpKShAYGIj58+e3Wl4ACyJEJIL7lbVQXCoFIEFp3zz8xmWd2CkREREREXV5oaGhrd7n/v37W9yHvb093n777VbIRhf3ECGidpdzuhjQSKC0vI4Z46bCSMKpiIiIiIiI2hd/CyGidqVp0CDjlAIAkOt0DnMGzBE3ISIiIiIi6pJYECGidqW4XIa6KgEq6T0M8XKGtcxa7JSIiIiIiKgLYkGEiNrVhe/yAQBX7c9i3vC5ImdDRERERERdFQsiRNRu7vxUjZ+u3YMADYSh5RjWa5jYKRERERERURfVLgWRHTt2oG/fvjAzM8PYsWORnJz82PaHDh3CsGHDIJPJMGzYMMTFxemcP3z4MORyOWxtbSGRSJCRkdGW6RNRK8lMKgQAFPS4gt+OnSlyNkRERERE1JW1eUHkwIEDePPNN7Fu3TpcvHgREydOxPPPP4+CgoIm26empmLu3LkIDg7GpUuXEBwcjKCgIJw7d07b5v79+5gwYQI+/PDDtk6fiFpJfV0Dss/cBAAUuF6Cfx9/kTMiIiIiIqKurM0LIlu2bMHLL7+MpUuXYujQoYiKioKrqyt27tzZZPuoqCj4+/sjLCwMQ4YMQVhYGKZOnYqoqChtm+DgYLz33nt47rnn2jp9ImolP168DU2NBHdNy+HnMxKmxqZip0RERERERP+nrKwM9vb2UCgUYqeC2tpauLm5IT09vU3HadOCSF1dHdLT0xEQEKATDwgIQEpKSpPXpKamNmovl8sf2Z6Ing4XTl0HAFxz+B5BQ4NEzoaIiIiIiH4pMjISM2fOhLu7u048JiYGI0eOhJmZGRwdHbFixYpWGWvcuHGwtLSEvb095syZg9zcXO15mUyG1atX45133mnxWI8jbcvOS0tL0dDQAAcHB524g4MDlEplk9colUqD2uujtrYWtbW12s9VVVXN7ouIDFd5uxrl12sgQIMeIyVwtHAUO6WnFuczIuosOJ8REXUcKpUK0dHR+Pbbb3XiW7ZswSeffIKPPvoIPj4+qKmpwfXr11s83qlTp/D6669j3LhxqK+vx7p16xAQEICcnBxYWFgAAF566SWsWbMGV65cwdChQ1s8ZlPaZVNViUSi81kQhEaxlrR/ksjISFhbW2sPV1fXZvdFRIbLOv1g75BC61zM8ZwhcjZPN85nRNRZcD4jIuo4jh49CqlUCl9fX22soqIC69evx+eff44FCxagf//+GD58OGbObPnLEeLj4xESEoLhw4dj1KhR2L17NwoKCnQekenVqxf8/PwQGxvb4vEepU0LIra2tjA2Nm60uqOkpKTRKpCHHB0dDWqvj7CwMFRWVmqPwsLCZvdFRIbRNGhw+cyD/+duuV7BBOcJImf0dON8RkSdBeczIuoKBEGApq5BlEMQBL3zTEpKgpeXl04sISEBGo0GRUVFGDp0KHr37o2goKA2ma8rKysBAD179tSJe3t7P/EttS3Rpo/MmJqaYuzYsUhISMCLL76ojSckJGD27NlNXuPr64uEhAS89dZb2tjx48fh5+fX7DxkMhlkMlmzryei5ivIKUfDPQlU0nvw8R0GYyNjsVN6qnE+I6LOgvMZEXUFglqD4vfE2Q/TeaMfJKb6/eytUCjg7OysE7t+/To0Gg0iIiKwdetWWFtbY/369fD390dmZiZMTVvnJQmCIGDVqlX4zW9+gxEjRuicc3FxadNNXtu0IAIAq1atQnBwMLy8vODr64tdu3ahoKAAy5YtAwAsXLgQLi4uiIyMBACEhobimWeewebNmzF79mx89dVXOHHiBE6fPq3ts7y8HAUFBSguLgYA7eYrjo6OcHTk3gREHUn6qR8BAHl257Fh8DKRsyEiIiIiol9TqVQwMzPTiWk0GqjVamzbtk374pPY2Fg4Ojri5MmTkMvlT+w3OTkZzz//vPbz3//+d7z00ks6bVasWIHMzEyd3/kfMjc3R3V1dXNuSS9tXhCZO3cuysrKsHHjRty6dQsjRozAt99+iz59+gAACgoKYGT085M7fn5+2L9/P9avX4/w8HD0798fBw4cgI+Pj7bNkSNHsHjxYu3nefPmAQA2bNiA999/v61viYj0VF1Vh1vZ9yCBBGYjauDc3fnJFxERERERdRISEyM4b2z+0w4tHVtftra2qKio0Ik5OTkBAIYNG6aN2dnZwdbWFgUFBXr16+XlhYyMDO3nX2+FsXLlShw5cgRJSUno3bt3o+vLy8thZ2en930Yqs0LIgCwfPlyLF++vMlziYmJjWKBgYEIDAx8ZH8hISEICQlppeyIqK3kpBZBIkjwU3cFZnoFPPkCIiIiIqJORCKR6P3Yipg8PT2xd+9endiECQ/2/svNzdUWK8rLy1FaWqpd4PAk5ubmGDBgQKO4IAhYuXIl4uLikJiYiL59+zZ5fVZWFjw9PQ25FYO0y1tmiKjrEQQBF089eCVXoctlTHKdJHJGRERERETUFLlcjuzsbJ1VIoMGDcLs2bMRGhqKlJQUZGVlYdGiRRgyZAimTJnSovFef/117N27F1988QUsLS2hVCqhVCqhUql02iUnJ2sf12kLLIgQUZtQ/liJunIJ1Ea1GDneHSZGJmKnRERERERETfDw8ICXlxcOHjyoE//888/h4+ODGTNmYNKkSTAxMUF8fDxMTH7+2V4ikSAmJsag8Xbu3InKykpMnjwZTk5O2uPAgQPaNqmpqaisrHzs0yMt1S6PzBBR1/NwM9Ufe2VgzfCXntCaiIiIiIjEFB4ejtWrV+OVV17R7vNpZWWF6OhoREdHN3mNQqGAVCrVPl6jL31eCbxlyxasWbMG5ubmBvVtCBZEiKjV1dXU48bFCgBGkAyphJuVm9gpERERERHRY0yfPh15eXkoKiqCq6urXtfEx8fj1VdfxcCBA1s1l9raWowaNQpvvfVWq/b7ayyIEFGru5amBOqNUGH2E+TjnxE7HSIiIiIi0kNoaKhB7ZctW9YmechkMqxfv75N+v4l7iFCRK0uLekaAOCG8yU85z5V5GyIiIiIiIgaY0GEiFrVnZJqVBcCGmgw0NseMmOZ2Cm1qdsFd8VOgYiIiIiImoEFESJqVZdOKwAAN3tcxeyRM8RNpg2V3ryHI9sycDAiDbd+uCN2OkREREREZCDuIUJErUbQCMhOvQlAivv9ijG452CxU2p19+/U4tyR67iSegsQACOpBGVF9+A0oIfYqRERERERkQFYECGiVlN0rQLCXSlqjavh5+chdjqtqq6mHhcTCpCRUID6Og0AYICXPcbP7g9ru7Z7FRgREREREbUNFkSIqNWcS8wFAFy3vYSXB64QOZvWIQgCrl+8jeQD13C/sg4A4NjPGhMCB8Cxn7XI2RERERERUXOxIEJEraKuph63su5BAmP0GAlYy57+YkFVqQpJ+6/hRlYZAMDK1gx+vx2Afp52kEgkImdHREREREQtwU1ViahV5J4vhqTeGHfMfsI0n0lip9MiDQ0aXDh2A7F/PIcbWWUwMpbAa7o75r/ng/5j7LXFkIslF/HmyTdxX31f5IyJiIiIiFqmrKwM9vb2UCgUYqeC2tpauLm5IT09vU3HYUGEiFrF96euAQCKXHLg6+wrcjbNd7vwLr6MSENq3I+oV2vgPLAH5q73hs+sfpCaGgMAskqzsCxhGRYeXYj/FvwX+67sEzlrIiIiIqKWiYyMxMyZM+Hu7q6NpaWlYerUqejRowdsbGwQEBCAjIyMVhlr3LhxsLS0hL29PebMmYPc3FzteZlMhtWrV+Odd95p8ViPw4IIEbVY5W0VagqNIECDIT5OMDYyFjslg2k0AtLjFfjXh+dRVnQfZhYmmLpoKOas8kRPJwsAwNXyq1j535WY/818nCk+A6lEisBBgZjZb6bI2RMRERERNZ9KpUJ0dDSWLl2qjd29exdyuRxubm44d+4cTp8+DSsrK8jlcqjV6haNd+rUKbz++us4e/YsEhISUF9fj4CAANy///PK65deegnJycm4cuVKi8Z6HO4hQkQtlp6cBwC4aX0Ny0e/IHI2hqu8XY0Tu69Aeb0SANBvtB0mvzQY5pamAICie0WISo9CvCIeAGAkMcIL/V7AslHL4GrpKlreRERERESt4ejRo5BKpfD1/Xmld25uLioqKrBx40a4uj74mXfDhg0YOXIkCgoK0L9//2aPFx8fr/N59+7dsLe3R3p6Op555hkAQK9eveDn54fY2Fhs3Lix2WM9DgsiRNQigkbAldRbAExQN+A23KzcxE5Jb4IgIOd0MU7/6wfU1zbAxMwYz8wdhMHjHSGRSFCtrsY/L/8Te7L3oE5TBwkkmOY+Df9v9P9DX+u+YqdPRNQigkZA9d063C2rQVWpCreuV8LreXdYWMvETo2IyCAajYDqylrcLatB5W0Vfvr/7L15lF1Hfe/7qao9nLlntSRbluchNjEOxo6HgO0gc/FNrkPCC1k3D5IHAbwYHuAHITY4l8kQQuJn3rphSggkYc4lLMINGJyAsY3FEJNgPBvbsi1Z6lZPZ95TVb0/9u7Tp9UtqVtqqYW9P1q1au/a++xT+/TR7+z61u/3q8cbXPp/nIZy1z8gwlp72N4Uh4rruiteCOD222/n/PPPX9R2xhlnMDo6yqc+9Smuv/56tNZ86lOf4uyzz2br1q1r2td6BNKUowAAIABJREFUPZ2YHB4eXtR+wQUXcMcdd6zpe/WTCyI5OTmHxa5HZqHpEqoul1567np3Z8VEQcJ3/v5BHv3JJADHnT7IFX9wFrWRIsYavv7o1/nI3R9hspsev2DjBfzx8/+YM4bPWM9u5+Tk5KwYrQ3t2XSA0JzJyvTi2mi76DVjW6r80iWb16nHOTk5OcujY0NzdrH9amV1YzqgNRtizWJ7dubFGxk/cf1XPYzjmA984APr8t7XX389nuet6NwdO3awefNi+1+tVrntttu4+uqred/73gfA6aefzre+9S0cZ+2kBGst1157LZdeeinnnHPOomPHHXfcEU3ymgsiOTk5h8X3v3sfAE+O/YxrTv2/17k3K2P66Ra3fOJe5iY6SCX41d86hef++haEFDw08xDv3f5e7pm6B4DjK8fztvPfxhUnXJEvtZuTk3NMkUR6WZFjfrs9F2Ltwa8zj1SCKNBHrsM5OTk5+yEKkp7tamUiR79d6zQiWIU9U66kNRMyfuIR6/Izjm63S6FQWNL2qle9iksuuYQvfOELaK35i7/4C6666ip+/OMfUywWD3rdO+64g5e85CW9/U984hP8/u///qJz3vjGN3LPPfdw5513Lnl9sVik0+kc4l0dnFwQycnJOWSiIGHy3i4Sh5FzXUpuab27dFAe/vEevvsPD5JEhvKgz3957TlsPHmAUId84ief4NP3fprEJpScEq/95dfyil96BZ5ambKek5OTs5aE3WSx2DHdXTRA6DYP7oItpMBxJToxS7xBlCvYfNogW84c4fgzhxg9voKQufCbk5OztlhrCTuZPduPgBu0D27PpBQoV5LEZok3iOsrjjtjkC1nDXPcGUMMbyofMxNZruty/fXXr9t7r5TR0VFmZ2cXtX3+859nx44dbN++HSllr21oaIivfe1r/N7v/d5Br3v++ecvWpVmfHx80fE3velN/PM//zO33347xx9//JLXz8zMMDY2tuL7WC25IJKTk3PIPPDvu5CJQ72wl6suumy9u3NAdGL4/v/6OT+7bScAx585xJWvPpti1eMnEz/h3dvfzeP1xwF40Qkv4voLr2esdOSMb05OzrMbay1BO140QGjsM1iIuslBr+P6iupIgcqQj+MpdGzoNCLmJjvEgcYaSxymXh/KlWw6ZYDjTh9k8+lDjJ9YQznrH1+fk5Pzi421lm4ztWeNfYTbeRs3b4cOhF9yqAyn9kw5kiTSdOoRs5MddGQwxmKy67gFxeZTB9l8+iDHnT7E2AlV5DEq6AohVhy2sp6cd955fPazn13U1ul0kFIuEpfm940xK7pusVjk1FNPXdJureVNb3oTX/3qV7nttts46aTl8/Pde++9nHfeeau4k9WRCyI5OTmHzI++9xDgMHH8w/zK+O+ud3f2S9CK+cbH72H3z9NkTc/7L1u54L+dTKC73PiDD/PFh74IwGhxlHde+E5etPVF69ndnJycZwD9CUv7BwaNvlnRZCUDhLJDdbhAbaRIdbhAdSQtftml0wiZ3tliz2N1nn5kjiRa/HDqeJKNJ/cJIFtrx0SCwZycnF8s5hOW7iva9tc6PvjguFh1++zYgk3zCor2XMjUUy12P1rnqQdmMMliDxCv6LDplIEFAWRLBalye7aWvPjFL+a6665jdnaWoaEhALZt28bb3/523vCGN/CmN70JYwx/9md/huM4XH755Yf1fm94wxv4/Oc/z9e+9jWq1Sp79uwBYGBgYFEozh133NHLX3IkyAWRnJycQ6Ix1SV6ysFiOOfi448Zt8R9mZvs8L//50+pT3bxCooXvepsTvrlUe6bvo933P4Onmg8AcBvn/bbXPu8axnw1z/5Vk5OzrHPkgFCX0hLYzqgNROik4MPEEo1rydyVIcLi0SP6nABr+BgrWVuosOexxrsebzO/d9/mpnd7SXx9H7JYdOpg+ms6WmDjJ5QQeUDhpycnINgtKE1Fy7jsdbNcnqEGHOQBB4CygM+tZEClcyO1UYWbFpluIDrKYyxzDzdZs9jdSYeq/Oz23YyN7E0P0Sx6rL5tMHUpp02yMhxlWPWA+SZwnOe8xzOP/98vvzlL/O6170OgDPPPJOvf/3rvOc97+Giiy5CSsl5553HLbfcwqZNm3qvFULw6U9/mj/8wz9c8ft97GMfA+Cyyy5b1N5/ne3bt1Ov13nZy152WPd2IHJBJCcn55D4wW0PArBr4BHe+tzfWefeLM/uR+t842P3ELRiKkM+v/HGcxnaXOIz936Gj/zHR0hMwnhpnPdf+n5+ddOvrnd3c3JyjiHmBwitvlCWRp/wsZIBghBQHvQXCRz9nh6VYR/HVUteF7RjJnc0eOw/9rLnsQYTO+qE7aXhM7WxIptOGcjKIEMbS3kOkJycnCXoxNCaDWlOdxcnK81Ka27pCi37IqSgMuRndqxPuM1s2nyYy750GhETOxo89IM9TOxoMLGjQbxM8uahjaXUlp06yMZTBhgYKx6zk23PZG644Qbe9ra38ZrXvKaXM2Tbtm1s27Ztv6/ZsWMHjuNwySWXrOq97Aqyft900028/e1vX1Hy1kMlF0RycnJWjTWWh384gcCDM+bYUNqw3l1awiP/PsG/feYBdGIYO6HKf33DL9NxG1xz67Vs370dSHOFvPvid+deITk5z0L6l6TtnwmdHywst4TjvkgpqAz7y3h3FKmNFCgP+Qf10EgizdTOFhM7Gkw+0WByR3PZ2VLlSjZsrbLxpAE2njzA+Mk1ygP+YX0GOTk5zwx0YhaJHIvyeGSCx8FWaJGOoDq0IHQseHcUqY4UKA94Bw1RiYKEqaeaTOxoMvlEg4nHGzSngyXnub5iw4k1Np5cY+PJA2w8aYBCZeXJP3OOHFdddRWPPPIIu3btYsuWLSt6zS233MJrX/taTjvttDXtSxiGnHvuubz1rW9d0+vuSy6I5OTkrJqnHp5GND0iFXDZC85f7+4s4SfffoLt//QoACf+8ihXvvpsfjz1Q6678zpmghkKqsA7LngHv3Pa7+SzDzk5z1CMnp8RXSp4NKa7tGcPviStVGJRCEttn7j38qC/KhduHRumn24x+USTvU+mA4aZXe1lPU0GxoqMn1TLygCjx1eOSALUqF1n5vF7mHrqYaYmdvP8//qHlMeWZvnPyclZP5YVPPo819r1gwseypU929Xv4THvtVaqeavyMIsjzfTOzJ490WDiiSaze5aG8iFgaGM5tWWZCDK8OQ9/OZZ585vfvKrzr7nmmiPSD9/3ede73nVErt1PLojk5OSsmju/8zMAntpwH2886S3r3JsFrLX88J8f4+5vpnlBfvny47n4Zafydw98ho/85CMYazhj6Az+/AV/zsmDJ69zb3Nycg6HAwkeK3UBl47oc/8u9oW1pPvlgdUNEPqZHyxMPZWJH082mXm6vWTpW0hj5TecWGPD1nTAMH5ibU1nS43WNHY9zPSTDzC9+wmmp6aZanSZCiR1WwIW7nHrz3/CSbkgkpNzVElDWoJFOYka012aUysXPBxXLgpj6Xl4jKaiR7HqHvIkUNRNmNrZYu9TTaYyeza7u72sqFwZ8tmwtcaGE6s9u+YX8yFnzrFL/u3MyclZFVGQMH1/jMRh03klXHVsuDhaa/n+//o5P/23pwC46KWncNavb+C67/8J33z8m0CaOPX6C6/HV7mbeU7OsU5/kr/G1KEJHsrJBgjDPtXRYm9W9FBnRPdHpxExtbPJ1M4WU0+lIsjcRGfZwYJfdthwQpWxE6rZoKFGZcg/bG81awyticeZfuIBZnbvYHpqLzONNtMdy4wukix65HOAam+vSMCoFzFacSlUBg+rHzk5OUvR2izKR7Svl8dKQloWBI/iUg+PkcJhCR7zWGvT1V52thbZs/re7rLnF2vegj07scaGrdU8lC/nF45cEMnJyVkVP/vRDmTiUPf38tuX/Pp6dwdIc5p874sPc9/tuwD4tZefzujzJa/85it5cOZBHOHwjgvewcvPeHkeIpOTc4xgTPrg3Uvy1+fp0ZhaYQ4PR/QGA/0x77XRtK1UXRvBYx4dG2Yn2kzvajO9q5V6gOxs0WlEy55frHmMbakwuqXaGzRURwqHbId0HNHY9RAzOx9hduIpZqanmWl0mA0sM0mBmH6BWgCV3p5EM6y6jBQtI7UyI6NjjBx3EqXNZxGqKo1Gk3q9RfGE3HsuJ2e1pILHYnvWL3q05w4eonc0BI9+kkgzszuzZbvaTO1MbVrQjpc9vzLkM7qlytiWCmNba2w4oUp5MBc/cn7xyQWRnJycVfHj2x8BfGZPfJwzR16+3t3BaMN3/uFBHvrBHhBw+f95Ju1TdvF7//v/YTacZbgwzF++8C85f+Oxl+skJ+eZzIEEj5Uu47gopGW40PPyOFKCR3/fG1NdZp5uZ6XF9NNt5vZ0lu+zSHN+jB5fScuWVPxY7UypNYbu7G5mn3qIuT1PMDs1wexcndl2yGwoqZsihv5VaST9ooewhqIN8G2EtBZrINGSIIYoNOyNQ6biEJnsxtH34+hbUSxeGrj9uney7YqLVtXvnJxnOvNJmHui7T6ix0pyEilXLgrPqx1hwWMeow1zk5k9291mZldqz+qTy3uxCSkYHC/17NnYliqjJ1QoVrw171tOzrFALojk5OSsmPpUB73Tx2I495KT1rs7GG249W/v5+d3TyKk4EX/11k8OvofXH/r9SQm4azhs/jI5R9hU2XTwS+Wk5OzKva/LO0qBI++pKVLBwqHl8NjpffQmAqY2d1mdk+b2d2ddHt3myQ2y77GKzqMHFdmZHOFkWzAMLy5jFc4+COVNYb6xFM8/fN7mdy1g7npvTSbLdqhpq0lXVEgEfuGIfpZme+0QcYhIg6RUV+JA0QUIbDsu0DvPldYlli4JMpD66XLYebkPNNZ4uExcwiCRxait8S7Yw1yeKzoHhJDfbLL7J52z47N7O4wO9HGJMt3vlB2GTl+qT1bbjnwnJxnKrkgkpOTs2Lu/G6aTHX3wKO84tzfW9e+WGP5zt8/yM/vnkQqwYv/6Bzu8r/Fh27/EADbtm7jxktvpOgcuXXLc3KeyaRJ/tIBwuLBwSqWpV2ySsvRFTzmiYKEuYkOs3s6Wd1Otyc7+x0oKEcytKnE8OYyw5vKjBxXYWC8QETEbL3B3FyTXfUdPPjTFu07m3TbbYJGHd2ZxnaboEOsNRgh0cpFKw/t+iD3HWhkHh59C8iIJEJEETIO0xItCCAiiXspUA2CWHpEykM7VWytgPAKCL+A4xdxCkXcQhG/XKZYKlOqlClXylSqFWrVMoODNQZqFQZqFVwnfyTMeeai475VWmaWLku7kpCWfsGjktX9IXtHymNtX8JOzOxEh7k9qU2b3dNmbqJDfbK7XxHa8RXDG0sMH1fJ7Flq00o175gPJbZxhJnZjZ7chZ6ewMxMoGf2oudmMfVZdL2ObrYwzQ66E6DbIbqbYELLSV/5Eu6p5673LeQc4+S/fjk5OSvCGsujP5xGUcA7q8OAP7B+fbGWO778CA/9cA9CCl78mnP4mv4sf/vjvwXgv5/533nHBe9AirVfojIn55lCEulFA4R965Uk+ZNKUBmeHxjss1LLaIHSwOqWpT0cdGxoTHeZm+wyN5GKHfWJVABp19McH9YmYEOsDcEGWBuBCBF+gnETEhkSE5CYkCTusntHgHgkQCUBSoe4UmBdF+N4WNfLahfjeljHwzouCAGl2gH7KpI4FTqSCKljhNGAxEof7VdxigO4lSKFcpliuUSxXKFSrVCtVRioVTMho8pgrYyUuZ3LyYmCpGe/el5rffasU18+z08/88vSLpe/42gKHpDa5/reLvXJLnOTnZ5Nm5vo0G0un+MDwPUVQ5vKDG8spfWmMsOby1SHC0et7/tiu2309NOYqd3omUnM3BR6dgpTn0M36phGA91qYVptdDvAdEJ0N0YHGhNaTHyo/RboyV25IJJzUHJBJCcnZ0U8et8eVKtAqDr8+mUXrGtffvT1x/nZbTtBwGWvPJ1PtW7mnx/9ZwDe/Ctv5tXnvPqYn/HIyTmSWGsJO8lioWMmoNW3faCH6nnmBwjzIsei0JbhwlHz8AAwxlCfa7PzsUkmnppiZs8srakGQb1B0mpjgy7WBH2Cx+La2hDB0nAQC9BWGNfFOh44LtLxcF0XW/AxThXjumjlEq3ArgirKZgQn5iCshR9l1K5QnV4lOHNJ7Lx5LMZ27iJYiFPRpiTsxKstQSteIk9698O2/sGii3F8dXinET7CB9HOqRlX6Jukooee7s0prrZdurp0ZoND/ja8oDH4MYyQxtLaRkvM7ixtCYrVs1jjcE2ZtCzE5jpiVTImNuLmZvBNOqpmNFsZEJGB9NZEDNMmKBDg4nA6sPpz8JrhbIoH1RBIYsuquShKkVkpYyqVVEDg8jBIdTQKGp4DDW6Efc5Fx/+B/EsY3p6mrPOOosf/ehHnHjiiUftfcMw5LTTTuOrX/0qz3ve847a+0IuiOTk5KyQ22+9B3B5etODXLz12nXrx398+0n+/Rs7ALj4d0/mf7Zu5I5dd6CE4t0Xv5vfOvW31q1vOTlHC6MN7XpEa6ZvcDATLtqPg4PngnALakHoGE7dwBeEj7UfIHS6AVPTdWbm6szO1mnUG7QaTdrNFt12i7DVJmm1sJ02hF1kFCCTAGlChFle0NgfFrCOi3WcrC5iHDf14nDdrM1N28TK4uWl1VRlwICnqfqKgUqRWm2AgZExamPHM7DpZEqjW5Aqj7/PyVkp8wlLW7PzIke4IOJmdRItn9OnH7/k9DzW9hVyqyMFCuWjK3jo2NCcDWhOpSE6jf56b3e/q7nM4xUdBjcUGdhQYnC8xNB4Wg9sKB4wZ5GNI8zsBGZmEj27F9OYwcxNYxqzqYDRbKBbTUyrjcmEDN0NMUGMCRJMqNGRxcSAXRsxA0A6FukLlC+RBQdZ8lDlArJUQlXKqFoNOTCAGhxGDo6ghkaRI5tQGzajRjYjiuXD6EvOSvngBz/Ib/7mb/bEkJ/+9Kf82Z/9GXfeeSdTU1OceOKJXHPNNbz5zW9e9bU/+tGP8uEPf5jdu3dz9tlnc/PNN/Nrv/ZrAPi+z9ve9jbe8Y538K//+q9reUsHJRdEcnJyDkq3GdF+RCKB0y/ZsG6hKPfdsYu7/unnAJz/307go/GN/GD3DyioAn952V/yguNfsC79yslZS6y1RN0kFThm5wcEYZa3Ix0ctOeig+bvAChW3XRAMC92DPXNig4X8EvOqgYIQRgxM9tgZqbObL1OY65Jo9Gg3WzSbbUI223CTgsddDBBFxulooZKurg6xLEHFjRUVvb72SAyEaOIdQvgegjPQTgC6QqEEmjpkAiXSHhp+MoKKRJQVTFVD6pFh1qlRLU2SG14jOroZmrjJ1EaPT4XO3JyVoG1lrCdLAgcs5lw22fb2vWDh+cBlAa8nj1bIngMF/CKR29YM++F17ufefvcy1ESpMtxH+S+ilWX2miRwUHDUKnDoN+hrJoURRsZNrDNOmaugdnZRLdbmFaL6U4X0w0w3Sgt4byIsRYeGfP0XUNYpAvKE0hfpWJG0UWVCshSAVUpI8tl5MAgqjaAHBhGDY4gh0aRwxtQIxuRw5sQbr5KzbFOt9vlU5/6FN/4xjd6bXfffTdjY2N89rOfZcuWLdx111289rWvRSnFG9/4xhVf+0tf+hJvectb+OhHP8oll1zCJz7xCV7ykpdw//33c8IJJwDw+7//+7z97W/ngQce4Kyzzlrz+9sfuSCSk5NzUO74zj1Iq5isPMmbfvU31qUPj/7HJLd9/iEAnrNtM3/jfIgf7P4BRafIx170MZ43fnTd63JyDpU41KnQkc2Izj9Qt2ZDmtl2HB7cE0IqQWXIpzLUNzCYFz+GfarDBRxv8eB9XtCYmJnk4Z1N6o0GzXqDdqtJp5mKGVGnTdydFzQ6iCjNoeEmIa498IymAAor+RCEjxU+QhbAKWIcH5Tf8+gQLgjHIh2NlQYjDQmCaMkKLAd5GwxlEVJWCRU3FToqpSLVao3K4DDV4XGqG7ZQ2XAibrFy8Avm5OQsIuomNGeDzMMjTIWOPpvWmgn2u2JTP8qRVIb3tWd+Zs9SMVe5R2cyxhibTgTNhbTrad2aDbI6pD3bJZqaQoVzFHQdz7TwdBPPdCjoNlXd5gQT4CQBjg1xiVEmRiYJIkkgSbCRwcQGE1lMAphUgIiyMntIPV8shAhpkR7ITMhQBRdZcJFFH1kqIMslVLmCrFaRlSpyYAhVG0IOjiAHR5FDY6iRTYjaMCLPVfSs4Jvf/CaO43DRRQtLr7/qVa9adM7JJ5/M9u3b+ad/+qdVCSI33XQTr371q/mjP/ojAG6++Wa+9a1v8bGPfYwPfvCDAIyMjHDxxRfzhS98gfe+971rcEcrIxdEcnJyDoi1lge/P4GiCGfNMVocPep9mNjR4F//9n6wcMYl4/xD5S97YsjHX/RxfmX8V456n3Jy9sVaS9COac9lD9JzIa25kPZsmqB0/mE67Bw81h3S5RArfQOC8qCHUxRoJyEWAa2wRavVotWYZKrdovtom/CeNlG33eehESDiLioOcHV0UEEDwMvKgTDCw0ofITyEKCCFj1UFcDys8rDKxSoHKyVWCawUWAVGGlAxyBgt9H68sfsHT5JFy68wL3IElJWm7FoqvkO56FOpVChXB6gMjlAZ3khlbAulkeOQzupElJycnDSRercVZ6LAgv1asGkR7dmAaAWheQDFmkd1yO+JG/uKH8WKe0TyEdmgg2lMYxozhFN7CSf3Es3MEs/MkszNkTSbmFYL221Dt4MNA0QUIpIEodPiJYbhxDCcgEnAJrCv+HDAPsCSpbAX2EfEUPMihkR6ckHEKHjIYgFZKiLLpVTAqNZQ1RqyNoSsDSIHhpFDY8ihcdTQhmd8iInWhrl2yHQzYrodMt2OmA4ipsOEmThhJtHceOnpDFXXP1+TtRZjuuvy3lIWV+wJevvtt3P++ecf9Lx6vc7w8PCK+xBFEXfffTd/8id/sqj9yiuv5K677lrUdsEFF3DHHXes+NprwVERRA4UL7QcX/nKV7jhhht49NFHOeWUU7jxxht56Utf2jtureU973kPn/zkJ5mdneXCCy/kr/7qrzj77LOPxu3k5DyrePyBCVSjSCQDtl1x9JOpNmcCvvHRe0hiw/G/NMgXx27mh3t+QMkp8bEXfSwXQ3KOODo2dJoR3WZEp5GVekinHtHOttv1iHY93O8SrvsiHVBFsE6CVTEJXRLbIdJN4rhOHM1h223EbIB8MFvhREcoDjzTKlmZh0YiXLTKRAzpo1wfpXyk9EC6gIMVLka4maghsRKstGkRGisTjEiwMgHRf98GCLNycBQJZRFRUpqSayl5ilLBo1wqUSpXKNcGKdVGKA9toDx6PIXB8TxsJSfnEEkiTacR0W3GdJqZLWtEtOt923OpfdvfEq774pecnrdaecjvCR+VoULW7uO4+/8/a4MOZmoC05jBtuqY5hymVcc065h2E9tuYtotTKeD6XawnS6628V0wyz3RYQNE0y84HlhM+HCmtWJLPNnW/aNdln+OtLJBAxXIvoFDN9DlnxksYgslZCVMrJURlZryEotFTBqw6mIMZB6Y8jBsWdlWEkQJcy1ImbbEbOdiLluzFwYMxclaUk0c9owZw11LHPSUlfQcMDsb6DvpOVNc91jQhAxpstt33vOurz3ZS/8GUqVVnTujh072Lx58wHP2b59O1/+8pf5l3/5lxX3YWpqCq014+Pji9rHx8fZs2fPorbjjjuOHTt2rPjaa8ERF0RWEi/Uz/bt23n5y1/O+973Pl760pfy1a9+ld/93d/lzjvv5MILLwTgz//8z7npppv4zGc+w+mnn8773/9+tm3bxkMPPUS1Wj3St5ST86ziu7f+J+CxZ9NDXHzi6hMoHQ5RN+Ff/uqndBoRQ5tL/Mspn+SHE7kYknNoWGOJI03YSQg7MWE7IejEhJ2EoB0TtNLS7dWp+LGS5KT9GEK07WBMG2uaoBsIXUeYJta00jaWXwJyPoeGDyAkCJUKElKBKpEIgZEOiZvm0OitiqIcUC5COdlrFFYoDAotJEZItBCpV7YwGGEyMcNgxfIiS6pxJOkwwILAZttZjUVYicBFWItEU7IRRaEpKE3FgbInKfkO5WKRYrFEsVKlVB2iODBCaXADxaGNeOXB3B17jTDGoOOEJE5I4hidGOI4RseaOEnQUUKSJMSxRicJSaKJo7ROtCaJNc+94JcY2rDymb+c9cEaSxTqni0LOzFBVoedJLNjUc+mdVsx3eYq7ZmAUsWhVBaUSpZyIaHkBBRVl5JoUTR1imYOFTZToWKii3mii+0GmCDABCHtMKIZxpgoxkYaE+lUsIgNJgaj6YWLrB3LXE9YpAPCARyBcCXCVWkOIt9DFnxk0UeVyzjVMs5AFVXJQknK8wLGUFbSkBJRHUY4z15n+0QbWt2YRjemFcS0goRGENOMNI0ooRknNGNDQ2ua2tCyhqa1NLE0paUpoakgVAf4+0uWcVlcfH45sQwmMGhgGMmQkAw5ihHXYbD87BOYDodut0uhsP9plfvuu4+rr76aP/3TP2Xbtm2rvv6+nirW2iVtxWKRTqez6msfDkf8f/FK4oX6ufnmm9m2bRvXXXcdANdddx3f+973uPnmm/nCF76AtZabb76Zd77znfz2b/82AH/3d3/H+Pg4n//853nd6153RO5Da43KZqSstWBBWwPWIoVESNH7gxpjiJOEIA6J4ogwDgmTGGMNUgiklL3XKCnxHR/f8/AcF095B3RrstZitMWYtE4fbBLCOCJOEqKsDqP5/Zg4iYnihDiJSWJNlMTpg1GSPiDFiUYnmiTR6MSgs9rodCUDrS1Wg9UWawAt0tqINPu0AZFtC5u1ZcZK2ExhFxYrLAiTzS5akBZcA45BuCC9NGFjbSsrAAAgAElEQVSTX3YoVX2q1SKDgxWGBwc4YeNmNlU34chn7w/PehC0YzoPpclUT7t47KgmUzXa8K2/uZfpXW2KNZcfnfcVtk99PxdD1gCdaKRK/5ZBlNqnUKf2wXUlnuOgsCir8VAIYTFxTNINiYOAqNXB6hBhQBsNOubJzi7AopSPUh7S85COi/R9XMdFuT6u56EcD8dxcBwfxyvhOh4CgU0kYRTT7YQ0OwHdTkgQRHS6Ed1uSNiNCaKIJNREUUISGZJIo+cfqmOLTSw2EaABLRBaIrRAaokwEmkkYhVuzv0YLLELsWMJPUvoCQIPuj50fUnHh3ZR0PUFkSqjZRUtSUUIBFoItExns4wQaAGGdNv2bRuRzUzu73dguQlbu8yB/Z7Xv7/M/Gd/k91nY8nrD9AHu7gW1iItyI5FtkA9DZIG0taRFlT2k6Cs7W0v3k/r9HuZtsvevt3nPItjbbrdO26y11gUBmUMEosyBoVBGo20BmkN1lqsBW0txqYDT4NFpz/5GLJ2a9GWRdsWem3Gpl9FYy3GCkx2zEK23d+Wrp9jEFkB3dvOvj/z+0KkIlff9rzoZdbARn+a+7n8Ny497OscDeb/VlKKNBlxHBLFAWEUoE2Eo1xcJVHKRUkHR7lIpUhiTRgaolgTRQatTfr3NpYwCrnv6XtBCKRQCKGQSuJIiVISz3XxXAfX9Sh6LgXPw3dcCm5a+46LtJIoComDkLDbJQkioiAgCiPiICDuRulzWRCTRAlJlKAjQxIZdGTQiUVHkMSgE4lOJEZLdKIw2kFrF2MPfaAnbIyXNNO8F3EDP6qnJazjhQ28oI7XreOFTaRdXjBNgGZWDrEXy3UMoVLRQijAkaAEKIVVCus4GOWipYtRHlp5GLcIfhEKZUS5iijXULVB1NAI7tAQ3tg4/vgGSqODlAYKuL46qivNrDeJNnTCmG6o6YQJ3VjTCTXdWNOOErqJoR2ndUcb2ommYwwdY+kYQ9daOtbSwtIRlo6AjoS2guBAQsY881GPSyIX9wkVspZKAjUNNQs1KxgQkkEpGVSKQVcx5DkM+i7DRZfhksdI1Wek6uN7x/7YQMoil73wZ+v23itldHSU2dnlM9jcf//9XHHFFbzmNa/hXe9616r6MDo6ilJqiTfI5OTkEq+RmZkZxsbGVnX9w+WIfoNWEy80z/bt23nrW9+6qO3FL34xN998MwCPP/44e/bs4corr+wd932fF77whdx1113LCiJhGBKGC667jUZj1ffyqXd8mbg1/8dZ/oHDYrDCYrHYbAYubTMYoVNRALtQp1NuqZAAQCYoIJBWpjNvViKsQiCQRiEPmH9/pSyfx1/Q8zBbdyzQyMqTtLlbPEjb2063MIMuzkBpmkJtkuHhXWwemmTAsatZTCBDIISkJ96I1GqL7AFIiPltByk9pPAQ0kNKFyl9lCqiVKmvlHGdAVx3CNcdzMoQnrcBpdbfXe9Q+N53/hNpHKbKu3jjxVcdtfe11nLHlx/hyftmcFzJk5d8n3+buQVf+Xz0RR99Vosha2HP/uaab5KoQuqBsAzCaoTRCGuyohFWI3vtfcewkG2nLw5SPwKR+hNYIUFIrBBYJFZkRToY4WCkwsj5p9+VMm/D3GUyTBwcYROU6SJNgDRdpA0QNgQbgA0wRBgiEpEQkRCIhFBCN/EIhEdXFWipIm3p05IlWqpASxZpqSJGHJoFPZT7+EVGZ2V9OMKf9sKcwDGFY5JMGDKZcJSKQSoTjhxrUFgKhaP3IHq49uxH3/o6d3+1mNmc/dizfW3WvC2zSd+2IfV4SmvdC02T2UpFou89MnsmVNouJFak9szK1KatzJ6tzROXNDFO3MZNujhJBzduZ3ULN27hxe1su40XNfGiBkoHq/+KSotQNu2yElglMEpglEQ7kkSpXomUS+i4hMojdFwC6dFxCnSVT0f5tJzMbqoiTVWm7pSpyxKB9EHI9Hcl/QXp806zPRFXYBF23+Okr9ECMWNhJkYwibCTveO9YkFk8rjIHsUltrdNX3t6LLu2YPF1IPuty2a+0waEEL3fwYUXiUzoFiAXvk/pPKLISvrdMtnr5gV0KwRGLuz3ikzF9VRwz4qAJNtPJJj5vCy9G2Nh9a1em1h03PYfz+5pkV3LzpfZMYnFN1AyUARKFkpA0UJFCipCUnEkFSWpuZIBTzFYcBgsuQwWfYaqPoMVn4LvoVwHqSTyGeY1KIRYcdjKenLeeefx2c9+dkn7fffdxxVXXMEf/MEfcOONN676up7n8bznPY9bb711URqMW2+9lauvvnrRuffeey/nnXfe6jt/GBzRse9q4oXm2bNnzwHPn6+XO+eJJ55Y9pof/OAHec973nNI9zCPtQsmcX8I5EIY9crCLtcEQyq49IrUWGGw2b7t7ScgNVZokBqEBpm2ITVCJgiR1TJBSI2UCUIlSKnTItI2pfr3NVIYpDRIYRDCIsTCjxYi/fyslWijsEZirEQnHknikegCOvYw2kfHBWxUwUYVZFTGiSu4cQVlFbVwlFo4CvXF9/+EjKiXdhNVd+IMPsno+AOcNLKb4jEUYu66wxQKmyn4m/ALmygWT6BSPp1y+XQ8b/SYnK1Ik6nuwaGMOGuOkeLIUXvvn922i3u/twsEdF/4KF+Z+xxKKP7ihX/xrF9NZk3s2QEGDwBWpLNx64Y1SJMgTdyrlYmz/bQoHSFNhNQxykRZW4ijQ1SvRKgkwNHdNNt/0kXpAGniIzZeFfPuDop00OD0DRqURCtFrBSxcoiVS6gcQuURKJfA8elKn45ToJ0NFlqqSEOVaKoyc6pMXZVpqNKqhReRDfCEsH33nm0Lu2hAkZ63cFwIszDYEJldJxuE7GPr08v1HwfsomftXj1/bP5Zu//o4v3ecGRhoNET3foGrD3RbX67fyBBb6BhZPppzA8mjOyLQ9/PAGHZAQF9DpErHUBY8K3Fs1Cw4BsoYCmSDSSAooCyTEtFCSqOpOoKar5iwFPUSoqil3o6uK5CKoXjOum24+A6Dsp1cFyF09s+FqY6lnK49kyiMmFi/1ipsGsymXSIWLPIlvXbM2GS1I6ZGGkihIkRdr4OECZE2AhsmJUulnnhtgsEaKmz/D5gpSCRgkRIur7EFiVItSDYCA8jNqJFAU2BSLqZSFGgKz060idQPi1ZpClLPfvTUiWSQxR7V/dZsfLn532NyrFO/70dfNGd/bxwdRxsGfO1RHO4HkOLkUYvCLgsePmpTLjtF3P7S6rXWRwsSsCHX3cFW884cY169cznxS9+Mddddx2zs7MMDQ0BqRhy+eWXc+WVV3Lttdf2xuJKqVV5clx77bW84hWv4Pzzz+eiiy7ik5/8JE8++STXXHPNovPuuOMO3ve+963dTa2Ao/ILuZJ4odWev5prXnfddVx77bW9/UajwZYtW1bU93m2vf65hFGQur5qQ2ISOrpLmEQIIdNQGCGyMYZASQcpFAoJIl3Cy1ibZRm2aZyvtZjEYK0hMZrEaLRJSIwGa3seJsamRcr5yQmLFAKEQSmFqxRKKpSUSCRCKqRw07AcIdLnsCykRwiRtiOQgiwEQmTeEKQPbNlDZ7qb/rO9B9q+z7hn2DN/FwvWGqwxGG3RxqT3qdPY5DhOSOI0NCdJEqIoJgoT4lAThwlxZNChRc9/hiQ4ootwIqwVdGVIKGNiDNZKnKRIORzGNR6jra3Q2gq7L4EH4H63TntsgtpWwdnnbOKiM56Dp/r99dIfGGvnnZezvmPAGqxNsFanbTbBmAhrY4yJMDbG6BBtumjdRuusTtrESZ04niWO57J6FmNC4niGOJ6h2bx3yXfLdYcol0+jUjmTwYHnMTj4fHx/fMl5R5sdj0zgzJWJZcS2F1141N736Ufm+P4/PgKAd/EcH2+n3mH/46L/wWVbLjtq/ThWWQt7dsJFT2F1OgiWxiBsNuQ0FisUWjjozJMjNgrjOBiZ5qCIrUQjscgsJCAVV+ZmO1hrkcIiyGZhhUEYnT6oCMic/FHZMSkSlNRIYiBByQRFAkIj0AhhsSYCDNZojI1TG6kTIpuQmBid2U1tNIlJSGyC1hrTazfZOQaTxTw4WqIMONriaFAG3ASc+VqnxU3A1eBqi5OAp8FPwEvS2knSdtm3VIo1Ig0n7FvMJf20NA6aRQcOA6Es0rHZ8rQirT2B8NLEfngSfIH1JNYXGF+iPYEpgPYFcUGQ+BAXLHHBEvkQFQ1hwWKlmc9GCHYhcENmf8dM62El3tLrTerhV8i8+gpIWUDJAlL5KFlEZm1WFElsmZgisS0RmQKRKRJZj8gUCLVLqD1C49LVLqFRBNohMIqOEXSNoWMtHSxdWORa3umLkV+Tb0AExU5EVUPVZK7lCGpCMqAkg45iyHUY9B2GCi5DRZfhss9I1WOw7KPUsTPzerj27MwLLmLvk58DnJ73hpAKrMQagRQWa9IQKWsNRmusNmAirEkwOsFonYb32swDwZLGO4l0Cjx9foL0IQyszNSHzEvEWNBKYoUhESadmceSSEuoNImE2EJgLbGyxNYQYomlIcYQE5HYhJiYhARNTLoXk4h0W4tsW0RowrQWcbZK03JDXsuCH9b+vnECQQlJBUQVIaogKlhZwwonXRlKumhZQskaQnq98C2b1Vpk4vqy4/VlQugOctri/YOEAdp9dg4SJih6+8uEAB4knHDfEMAl77nPtaRNfyUFaRje/G/f/PZCrdOa1Hup1947lnotSVJhQFiDsqno3AsBtCCNRViBMlloorFIA9KAMOl5UgNWYkz6f8Nm/0esEZhF22noXhr+J3rfJGvTsL758D7LQmhfL+RP7BvmJ7M2ueDVQpbPSh5YqjEyzYF1uPYy7K4suXdOynOe8xzOP/98vvzlL/eiLv7xH/+RvXv38rnPfY7Pfe5zvXO3bt3aS366Y8cOTjrpJL773e9y2WWXLXvtl7/85UxPT/Pe976X3bt3c8455/CNb3yDrVu39s7Zvn079Xqdl73sZUfsHpfjiAoiq4kXmmfjxo0HPH/jxo1A6imyadOmFV3T9318//BCFk4+5czDen3OoWGtpdXs8sSup9m5e4LJiTkaUx2CWYOYKeBYl0REtL0GsQpQxqUWjlCOByg/PQBPw6Pb4d/L25GntDjvolO5/DkXU3BWsg7D2vQ/SeoEwW6C8GnCYDdB8DSd7uO0Wg/T7T5BHM8yN/cj5uZ+xM6dfw9AsXACg4PnMzh4ASMjL8T3NxyV/vbznW/9BCgwsfERfnXrytcZPxzacyG3/PW9GGMpnRVzk/4fIOAtv/IWXnraSw9+gWcBa2HPrnr1NQc/6RlKrGNacYtm1KQZNWlEjV6ph/VemQvnqId1ZsNZZoNZ6mE9C3VcitIWNxNKeiWGAeMzRpER4zNsXAa1w2AiqcaCSmwpR4ZCqHGCCBvF2DDCRAkmStLkg7HGRGluFJOA1X3CixZoLZZZyMVwsOnHg80aCmWRLkhXIF2VJhz0HaTnQsHB+i7Gd0gKDoknCT1J4As6nqXtWxqOpu4mTLshU07AXqdD2zEoKXDE/Owd2TY4ffuOsFkNBelScX1KyqWkPApK4UuFJyVudq5Eg03QOsCYMBOvFxLWWpugdQutW8SrfLJ2s7LfhSt7kTcSpUo4qoxyFkIo05K2W1EmokZgygS6RGDKdGOfri7QTnw6iUtbO7QSh7axNIyhlcXtN4WllSUfbDvpd6DrCLoOTC7cKYsGwBroZKW/y9ZSS2BQw6ARDCMYkpJhx2HYVYz4LlecOsbxY5XVfViHyOHas+rwMFe99k1r2KNfLCId0YpbNMLGgk2LGzTC1J7NBrNMt2eY7s4wF8wxF87RTBp0TBuwWNpo2mAnFgb1+8SyzdsLL5IUQ0UpVBT7S6AoRS6FyKMY+7jWy7xSVFZnYZNSpt6HQmKVwgiHxPEwjoPNinQUKIVwJFKK7P9YOtmY5leyGJF90y3ECBILMYoFP7b9OI3Yfu82WJwcOq1TMULjyBglotRbWsUYFaNFSKIiYtHFyhArQ0xWowKsCHCkRooYV2YBUX32rKRcyo5PyfEoKpeidPCVwhOiZ/skGkyMNkE6AWdC7AqWSl9LhFB9NmzBnqX2bd6mVdI2J62VU8ZRFRyninIq2XYFpSrIA+T+04kmiWK0TpM7G51OmJrEEMdJmvhZ6ywZdJb4OUrQ2ixKBB0nSZZXUad5EbUh0YZEa8a3bDyKn94zgxtuuIG3ve1tvOY1r0FKybvf/W7e/e53H/A1O3bsYHBwkHPPPfeA573+9a/n9a9//X6P33TTTbz97W+nWFx53pO14IgKIquJF5rnoosu4tZbb12UR+Tb3/42F198MQAnnXQSGzdu5NZbb+3FF0VRxPe+9z0+9KEPHcG7yVkPhBBUayXOqZ3KOWeduuR4s9nh3oce4dHHDFNPWeKnXZR16DpNOm4DZV1qwQhD7Y1wDzx2D9xd+grilCaXXPEcXnDmRUc0UagQopdPpFo9a8lxrQM6nUdptR6m0fwZ9bl/p9l6gG7wJN09T7J7zz8BgoHacxkbu5KxsSsplU48Yv2dJ2hHtB9UKOC0i49OSI9ODLd88l66jYjCBvir2g0g4JW/9Epedc6rjvj75zw7cJXLkBpiqDC0qtdpo6lH6QBjJphhOphmppvW093ptK07zVR3iqnuFJGJSFeSWX41mX4KqsBocZQNpZMYK40xVhxjQ2kDY6Uxxkvjvf2i8LDNmXRJyuYMpjGHbTfS5Slbjb6lKdtp6XaxnS4mCNMSRtgwxoRJutJDpNNktDGYBOaHEanYAjqABYElBrrL9t/LSu1ANyn6PFo8AZ4AV2A8QeIJItcSuNBxNU0noeUaWr6m7QXs9CUNH+q+ZM4XzBQEs77AZDHmRafIeGmc8dI4G0obGC9tYGNxhPHSMGOFQUYLNUrKwdgIo7vpYEOHGBOgdTerA7TpZse7absO0KaTHtOdrK2THe9gzLwaZXqiywr+3MCC2LLkM3NASh/HqeE41bSoKo6b7iMGCKnRSWq0kzLtpEwrKdKMfVqxTyNRNLRlzmRLVArLnISGgo6TztDOuTDXc5jsE1IsEMA/7PGPmiCSc3h4ymNYDTNcWN2qQLGJqYd1ZoKZVDTJbNi8bZu3ZdNBWicmIfIMkWeoVw88QHeMQzEpUog9CqFLoetQCASljqDcsdS6lkogcMzhP3vFwiV2fLRTANdPk3h7Dq7n4PkK3xG4DrgSpLI9Tw1jLYkxRBoCLekaRWA99BKpeD7Py0oGZxYhIoyKiFVMoCKaKqIpA7oqIJQhoUpLJEMCVcfIpeJ1xa0wXjqODaUNmT0bY2NplPHiEGOFQUYKNYpSYUyY2awg2w4ym9VdXPdsVpDZrwV71rNrpoMxqfGyVpMkTZJkbYJfpCxmtmzeplVwnBquU8vaaj375joDeIWBLB/fAI5TQ6wqv1jOWnDVVVfxyCOPsGvXrhV77N1yyy1cf/31vTCbQyEMQ84999wluUSPBsLaff3C1pYvfelLvOIVr+DjH/94L17or//6r7nvvvvYunUrr3zlKznuuON6K87cddddvOAFL+DGG2/k6quv5mtf+xrvete7Fi27+6EPfYgPfvCDfPrTn+a0007jAx/4ALfddtuKl91tNBoMDAxQr9ep1Q74CJfzC8jE5DQ/uPsedjywl3inS6FTpeFPY4RmIBjrJaY1aCbHH+WUS4d56QtezIA/sM49T0mSJvX6T5ib+zEzs9tpNP5z0fFy+XQ2jl/Npk2/g+8fmeR3X//KXTx5a8BMaTdvvPEqhoqHbuBWyu1ffJif3bYTpyD4x3M+zIT7FL9x8m9w46U3HtXVbY40a21/cnt27GGtpRW32Nvdy3R3mr2dvezt7mWqO5XWnbTe29lLM175Q2fFrSwSSjaUNjBWXNjeUNrASHHkkFbjssZg23VMfQrTmMHUZ7GtuVR8aTUwrWZadzqYThvb6WA6XUx3XmyJMUG8ILREFhMv9mpZa4RjwLHgWoxrMS4kXroaUORBkJWuD6HvYCplVG0Id3CU0shmKqMnMDR+KuPHn8PY0NZV2xlrdd/Aoo3WHZKknYooSTs71ibJQioT3UbrFknSStuTVm8/SVoYs7zgtFqUqmRC/ACuM4iTJfi2coiOHqKVDNCIKjTiMo2oQD32mIlhRmtmreX9v7yVs7YMrui9cnv2zMdaSyNq9OzX3k5m17LtftvWjtsrvm7FqTDoDFERNUqmTCEu4kU+XtfF7UicFrhtiw272DBAxAEqCXCTEHeNvCb6BRXrFhC+j+e7qZjiKTxH4CqLIy1KpOJwGuZuCQ10taJjXKKl68KuCCtitAwJnIi2CunIgEAFhCrs1fPbsYx7ri9lt8ym8iY2Vzb36s3lzWld2cxIYWTVE1nGJBjTJdFtjO727Na8HdO609c2b9dafXUztX+6RZI0+wTjwyMVTwZx3IG+BQsGcd3hhcUL3OFsQYMhPG8YKQ/N62yt7E8QBDz++OOcdNJJB1zCNufocbC/yRHPIXKweKEnn3xyUSbhiy++mC9+8Yu8613v4oYbbuCUU07hS1/6Uk8MAfjjP/5jut0ur3/965mdneXCCy/k29/+9orEkJxnPuMbRrj6JZfDS9L9hx99gttvbzF7v0YEkro/hRWGwWADGydOp/0V+P9u+TruuS1+56oXcdroUk+Uo4njVBkZeSEjIy/kFCAI9zC191/Zu/fbzM79kHb7YR597MM89vj/y+jor3Pc5t9jePjSbJWcw0cnhkfvnMWliPPcxlERQx76wW5+dttOAL5/xj8y4T7F8zc+n/de/N5nlBiS8+xACEHVq1L1qpw8cPIBz+0m3Z5AMtmdTAcYnb1MdCaY6k4x2ZlksjNJJ+nQilu06i0eqz+23+tJIRktjPYEkg2lDYyXxxd5UGwobaDkLs52L6REVIeQ1bX9/27jKPVmmd2biS1zqeDSrGeeLg1MO/NoabcxnQDTDTKhJUq9WUKNiUwqsiRkmUzBJjJd+zNIxwnzrv37fxTuZuVp4J5e6wwwKy3CteCBcAWiIBEFD6fo45YrOJUyslJBVarIag1ZG0AODKEGRpCDo7hDYxRGNiEGzkIc4uoIxsR9Aklzn9LISpM4aZAkdeK4nh1Lt7VuAfS8VYJg537fq5SVeWdyVShlA4phNtVuAJ7dyatzFhBCMOAPMOAPcMrgKQc8txN3mOxMLhJL5m3YfNtkZ5JAB7SSFq2ktfgCDlDNyv/P3n2HR1WmDx//TstkkkwmPSEFEgEpEiJdUBEsgChYXtYColgQd4EVEddVF0VXxbWuu5b97S6LBRVdBcuuIih9kSqRhABSEkogpE0mZfrMef8IGROSkElIyCTcn+uai+SUpzDMPZz7POd54kGr0hJtiK6OX6E1I8DiidRFYfCEoXXq0dqCsJbbqSivoLKiHFtlFfaqKpy2Ktw2Kx67FcVhReVyoHHZ0XrsBHmrEyo6xYXO5QJXZb0BcP6M73OqdXg0v4xO0Qbpqkeo6FTotCo0GoWacOABXIoah6LBjh4PGlSKDq1HR5gHmhqT5cWDQ2PHqv0lYVKkKeKo5ih2rR2bxuZLoui1ehLDEkkKSyIpLIlkYzIpxhSSjckkhyXXi/8AarUWtbp6VFpr8Hqdv8QyT3Uc89SKaa6amOaq/rNuXCvH46lOrtXEPuz+112dFK5OjlzU9xVCQtJapU+i82rzESKBSO5AnL/2Hchj3fqdlGeB1h5MRbCZcHs0WqV6/HBVUBmuASeYetNEukY0b6LKc8HlslBUtIrjJz7GYvnRtz04OImkxMkkJU1Gpzu7f9Mb1uxi18fFVOks3Lwggx7RZ76gO1tFRyv47MUdeFxejl64k/9Gv0O38G58MP6DgBm105rkjqpoiSpXle/CotBayEnrSd/FRc3vxbZiPIp/i9kag4y+JMnpCZOa38ODwgNuBSzfSBZzEd6yIryWEryWUrzlZrwVFjwVp0azVFVWJ1iqbHisNtxWG55TCRbF6UVxguJStf4IFpWCWgtqvQqNXl0994pBh8YQjDokGHVYyKmkihF1uAmNKRJ1RHVSRRMZgzo6AU10IipDozOXNMrrdZ+6eLCcmtj71MtdVmeib9/LacbpKq03R8HAgUuJjBjiV50Sz0RzKYpChauCwqpCCm2nkiWnksC1Y1yJvQSv4t9yLBH6iHrx6/T4Fhb0S8rB6XRRYi7HXFZOWZkFS1n5aQmVSpzWKlw2K167FcVpQ+VsvREqCtWT87p0BgjSowrSodZVrwil1qpBXT3niketrR7FovJ/BIqCgl1j971sWpvvT5vGhk1rwxASTFJkEinhXela8zJ2pVt4N0J1zY89bcHrdZ2KZeW43GbcrtpxzexbwMDpKq0T1xTFXaecEcPXYDB09atOGSHSeTX1nkhCRL5wz0sej5fVG7eQufoIhpMxlOuLCfIYMLirvzDLDIVoh5UybcL/Iz60/Vd8aUhl5T7yj39MQcHy6uw51Vnx5KQppHS9B31QTLPLVBSF1x//Cp05jKJ+OSyY1baTqTpsbj55bivlxXZsXYp4t9tzGPVhfDj+Q1JNqW1ad3uRCwjRVjxeD6X2Ul+C5PQ/T1ad9I028UftuTlqLioSQhN8vyeEJGDSmwIuadIcisOOo/Q4JSf2UnryAOVFh7GWHMdhKcZjKUOprERnc2JwgN4FQU7QOUHrVKFxqlC5VOBSobhUvpErrUGlUVAHUZ1UCdaiCQlCHaJHE2JAbQxFUyehEoUmKhZNVFx1QiUmCVV4lF8jVRRFweOpxOmsXgnN6SolwjQYnc6/ZLTEM9FW3F53nVFyJ60n6yRNauKZ3ePf0IFQXeiZ41loAkad0a94Znc4KSkto9Rcjtlsoby8goryCqrKK7BWVeI4lVDx2KtHqOCwoa555MfjQOtn4rqGolKhaHUoGh1era76Z60ORacDnYZQeRIAACAASURBVBa0OrwaHR6N7tRqS03zqDzYNDasWqsvUWLT2tDpISo8nMSYZLrF9yQt4gLSTGkkhSW16HHMc6l6IYNyXyxzOUuJjh7p9yM0khDpvCQh0gD5whW17TuQx7f/3Yay14hNV4neYyDIUz2BVlHYESJGObl/7B0NDjEMBB6PnZOF/+HIkX9SVVW9XK1aHUSXLr+iW9fpGAz+j3TJ/imXdW/n4lI7GPG7OIamtt2waUVRWLloNwe2F4LRxeJeT+IOcvC3a/7GJV0uabN625tcQIj2Vums9F1cnKw6WS9pctJ6kjJHmV9lBWuC69yRrbnASAhN6DRJk0pnJUcrjnK04ihHKo5wpPyI788iW1H1QV4v4S6FGJtCpEMh0u4lyqEi2RtGvDeEaFcQ4U4NBifo7G4UqwOv1YHX5sRj9+B1ePA4QXG30t+TWkFTO6ESeiqhEhZyKpliRGOKQBMZjSYyFnVMAprYLmhik1BHdUGl9e/CR+KZaE8185v4YljVL7GswFrgi2cVTv/majJoDb8kSU6LYzU/+5s0OZPKKislZgulpeWUWcopt5RTWV5BZUUF9qoqHFVVuOxW3KdGqOD0L6GiAIpGeypZEnQqeRKEotXh1QX9kkjR6ho8/3RexYNLZcepsuFQ21FrXeiD1USYwklJvID+fYbTJzkDvebsVr4LFJIQ6bwkIdIA+cIVDSkqMfPJR9/jyQ6nKshCiNuI1ls9TPFw4k+MnzyIUT1GtnMrG6coXoqLvyfv8N98E7GqVFoSu/yKtLTZ6PVNj3T563Ofoz4aTkFqDn/8fduODtm9IZ+1H+wDtcKyvq9RaDzM/Evmc0uvW9q03vYmFxCiI7C77b9cWFQV1Pmz5iKj1F7qV1nBmmDfxcTpSZOanwPx8Rx/VLmqOFJ+hMPlh8krz6t+WfLIteSecSROYmgiF0RcQM+InvSI7EHPiJ6kmdLQe8BbWoCntABvaSEecxHeshK8FjMeSxmecgveiko8lVV4K614bA68VhceuxuvQ8Hj5KxHqqQ8eT9hk/2b5V/imegIrC5rnSRwQVUBBdYCXxKlwFqAxWHxq6wQbUid2NXQz2392ElllY1SczmlZRYslgrKLdWP/FgrK7FVVuKwWnFaq3Dbq/DabdWP/PjmUKl+5EfR6vBqg04lToJQdLpaPwf5nTTB7UTldoLHgcrrQO31olZp0GjCwBCJLtSEPiQEQ2gYoWGhhBrDMIYbMZmMRIQbiYo0ERYaXGc+yfYiCZHOSxIiDZAvXHEmR/JPsOyjDWgORFIVVIbRGQ1Ala6MquEHmX3T3cQYmv84yrmiKAplZVvIy3ubUvNGoHoZx5Tku+jWbQY6XcOrB+QfLebz53ah4KX7AyquvfiqNmtjSX4l/35hOx6Xl+2pX7O9y7fc0ecOHh36aJvVGSjkAkJ0Fg6Pg8Kqwuo7sbUuNGonT/xNmoRoQ+rcifW9av0eqKP0GqIoCoXWQnLLc8m15HKw7CC5llwOlB1o9O9ErVLT1diVnpE9uTDyQt8rKSzJ72SR4vWiWIrxFB3HU3y8OqlSWoinrBRv2amkSkUl3ooqPFU2PNaahIoHj6N6VaBur88nZOxkv+qTeCY6C5vb5kv2np44aW7SxKgz+h7DqZ0oqYlp8aHxGLT+LOXbNlxuN+aySkrNZZSVVVBeXk55eSVVFdVJFUdVFc6qchRbGTitoLjwouBVa/BqgvBq9Si6IFA3vSSuyu1C5XSgdlW/VE5n9c9OByq3ExXgQY1bE4TbV3YwqqBg1PpgNHoDOkMIQYaQU4mVUEJCQwkzhhEWFkr4qcRKhCmMcGPoWSVWJCHSeUlCpAHyhSv8se9AHv/5aAva4yZcGqdvfpHDsVkMmdSVSf1vDPg7muaybRw8+BIWyw6gegWbrl2n0zXlbjSauhcXf//rV7h2h1IQt5+nn57eZqu7uJwe/r1wO+YTVRRG57Ks5+sMT7yEt65+K+CfT20NcgEhzicOj8N3kVFQVVAvYVJQVeD34zm1LzJOT5bUXHgEawP/P59mu5lDlkMcLDvIfvN+DpQdYH/Z/kYvtkJ1oVwYeSG9o3rTO6o3vaJ60TOiJ0Gali31eSbeSgsqnR6V3r+/R4ln4nxSe6RJY/HM38dzTHpTgzGs9s9t8RlvLTabnRN5P5N7cCd5Jw5QbCmlyu7G5dGCEoxWMaBRNfF/Oq8XleuXBInaaT+VNDmVPGnmJaoXFW61DrcmCI9Wj6LV86u5j9Kv75lXR6ohCZHOSxIiDZAvXNEc367ZRPbyYryKl2BXGGrU2LSVmEdk8+ikWYQHBfa/IUVRKClZw8GDL1NZtQ8AvT6B7hfMJSHhJlQqNZayKt59/H9ovFoibrUwZfRNbdaeNe/vIed/J3AF2/ig37NER5pYet1SIoIbHrnS2cgFhBB11dyZLbAW+C4yat+ZPVl1kgqXfxcZkfpI30iT2smT2o/qBOJFhqIolNhL+Nn8M/vN+/nZ/DM/m3/mYNlBXN76K1poVVrSItLoG9WXvtHVr15Rvc75XWeJZ0LUVeWqqjO65PSYVlBVgM1ta7ogIDo4uuFRJqe2xYbEolP7+WjLOaQoCgWVJ8g5uo2fD//EkaJ8SsorcTg0hLhDCXGFEOoORc0ZbrwpCsGKnSCvE43Xg9ej4HYpOJ1eXA43uF2o3Q60bgc6rxM1DV/Ojnv6dS7qLQmR5igpKaFPnz5s3bqV1NTUc1r3kCFDeOyxx7j55ptbtVxJiDRAvnBFc1VUVvHe4m/w7Dbi1NoxuI0oePn5gh+YPu1G0uPS27uJTVIULydPfsXBQ69itx8DwGi8iJ49Hue/X1mwbNJRYjzG7xbe0mZ3WfdvO8nKRbtRUPiq75uURB3h/Wvfp090nzapLxDJBYQQzVczEezpFxa179I29yKjseHsMSExAXOR4fK6yLPksc+8j70le9lr3sve0r0NjiZRq9RcYLqAi6IvIj0mnX4x/bgw8kJ0mrbri8QzIZqnZtnhOomSWiNMan52eBxNlqVWqYkJjqk3R1OdeGaIQePHoy3ngtPj5GDZQfaZ97GneDcH8vdSUFKE2qkj1BVKmDus+k9XGDql8bilwkuE2kp0sEK00UBUdDRhUYloorpi10VRUVlFZXklVVVVjBt7BWGh/j1uKQmRavPmzcNsNrNo0aI629955x1effVVfv75ZyIiIpg0aRJvvPGG3+X+4x//4L333iM7OxuAQYMG8fzzzzN06FDfMV9++SXz5s1j7969rTqvjCREGiBfuKKldvyUw+r394BdS4jbCMCxiH30vcXEHQNuD/hHaAA8HgfHjr1Dbt5beDyVAFhOpFO88xa8I+CBm/17dry5LEU2Pn5uKy67hx1J37Kt69c8f9nzTOg+oU3qC1RyASFE66tZbaKhC4vaFx1Or7PJsk6/yGhoKHusIbbdLjIUReGk9SQ5JTl1XiX2knrH6tQ6ekX2Ij02nf6x/ekf058UY0qrfVdJPBOi9SmKQpmjrF4C+PTY5va6myxLo9IQGxJb5/Gc0+NZtCG6zR6TbopX8ZJfkc+e0j2/xLPiHOx2uy85EuYOI8wVRrgrDKMrDPUZkiVBOInW2okJ1RITZWLA1bcQntTTr7ZIQgRsNhuJiYl8/fXXDB8+3Lf91Vdf5ZVXXuGll15i2LBh2O12Dh06xIQJ/v8ffsqUKVx66aWMGDGC4OBgXnzxRZYtW8bu3btJSkoCwOPxkJiYyDvvvMO1117bav2ShEgD5AtXnA2nw8W//vUV9l0G1GjQKFoqgsyYR2bxh4kPYwwytncT/eJ0lnAo9y8cO/YhKpUXr1dNfOKt9Ok5r9GJV1vK6/Gy/JUfKThUTmF4Hsv7vs7kvrfz+6G/b9V6OgK5gBCifSiKgtlhbnA4e+15AJpzkdHYqjkJoQlEB0ef06RJobWQ3cW7yS7J9v3Z0EiSCH0E6THpZMRmcHHcxaTHpLd4wlqJZ0K0D6/ipdReWh23GnjksGYpYk8jS/TWplVrfUsN13vk8NTPUcFR5yxpoigKx6uO14lnu0t2U+WqAgX0Hj1Gl5EwVxhx7gjiXUZCHAY8nlCU0x7DmXn7eGJ7DW2kprokIQLLli1jxowZFBUV+baZzWaSkpL46quvuOqq1ltwwePxEBkZyRtvvMGdd97p23733Xfj8Xh47733Wq2upt6Tzj+DoRCtLEiv44Ff38y6/21nx0cFuBUnRmckId+P4HcFz/GHabNJCktq72Y2KSgomqQuj7LmnW4kXvQlYYlZFBV8hLn4a9LSZpOcNAW1unWetd/+dR4Fh8pxaR2s7PEOA+Iv5uHBD7dK2UII4Q+VSkVUcBRRwVGNPqZ3pouMmqRJzUVGzfafin5qsCytSlt9Z7aRhElr35mNC4kjrmsco7uOBqovKo5VHiO7OJtdRbvYVbyLPSV7KHOUsSF/AxvyNwDVyZ0LIy9kQNwALo67mGFdhhEVHNUqbRJCtA21Sk2MIYYYQwz9Yvo1eIzH66HYVvzLCJNaMa0mnhXbinF73eRX5pNfmd9ofTq1jriQuDPGs6jgqFYZfaZSqUgKSyIpLIkxqWOA6ticV573Szwr2sXP5p/JU/J+OU9RYXKb6KNOIdUVSYRNhy4x9azb0xoURcHq9bZL3SFqtd/vy/r16xk8eHCdbatWrcLr9ZKfn0+fPn2oqKhgxIgRvPLKK6SkpLS4XVarFZfLRVRU3e+boUOH8uKLL7a43JaQESJyB0KchaISM+/95XsoDibYU32HbVfqambecwv94/q3c+ua9o+/fYUzM5Sy0AKmPhLNicOv+yZeNRhS6dnjUWJirjmrL7gTB8pY/sqPKAp81/NdylOO8fGEjwN66eK2JHdUhejYPF4PJfaSMz6eU2Qrwqs0/Z/fc31n1ulxsq90Hz8V/cRPRT+RWZRJQVVBnWP+POrPXNXNv7uAEs+E6NjcXnd10qT2EsOnxbNiWzFKI5OW1hakDqozl0lDiZMIfUSrPbJnc9vYU7LHF892Fu6st6z5h+M/JD3Wv3n+2nKESJXHQ/f1WS0u82wcHJlOqMa/0Yo33ngj0dHRdeYPeeGFF3jyySe54IILeP311zGZTPzhD3/g2LFj7Nq1i6Cglt08nTlzJt9++y3Z2dl1Rm18+eWX3HTTTbhcrlabR0RGiAjRhmKjI5n71P/jw6UrObnRht5roH/elfzjza+YOK2Qa9Kubu8mNurI4QLsPwWjBrqNNdA1YSwp8Vdz/Pi/OXjoVWy2PHZl/RqTaRA9e/wek2lgs+tw2NysWpyDosC+mK3kxe7iX6P+dd4mQ4QQHZ9GrakejRES1+gxNRcZZ1qesz3uzAZpgkiPTSc9Np07uAOAgqoCMgszySzKZGfhTjLiMpr/lyKE6JC0aq0vpjTG5XVRZC1qOJ6dGnVSbCvG6XVytOIoRyuONlqWXqM/Y8IkPiQek97kVzwzaA0MjB/IwPjq/58qisKximO+WLa7ZDe9o3o3/y/lPGaz2eolDLxeLy6Xi7/85S+MGVM9Yuejjz4iISGBNWvWMHbs2GbX8+KLL/LRRx+xdu3aevUZDAa8Xi8OhwOD4dysnCYJESHOkkqtYsrksWzrvZsN/zpEkDuE3ieH8+3fs8i/9Th3ZUwNyMlWly3ZiE6JoigulxlX3wWASqUhKek24uOvI+/w/3H06L+wWHawfceviI0dS4/ujxASkuZ3HeuX7qOixE65voSNaZ/y0KCHGBA3oK26JIQQAaH2RUZGbMMJBn/vzLq8Lr+SJr5RJrUvNGqNOInURzb4XZQQmsC4tHGMSxvXav0XQnQeOrWOxLBEEsMSGz3G5XFRaCus/2hOrZ9L7aU4PA6OVBzhSMWRRssK1gQ3OdIkPCi8XjxTqVSkhKeQEp4ScBP2h6jVHBzZPitShjRjlEVMTAxms7nOti5dugDQt29f37bY2FhiYmI4cqTx97ExL7/8Ms8//zzfffcd/fvXH01fWlpKSEjIOUuGgCREhGg1QwZeREpSAh+8tIGgqlDSzOkcXJLLc+YXeHzUo+02g3dDtmzPRnc0Ci8eRv2qL1p13VCg1Rrp0X0eyUlTOJT7OidOfEZR0bcUF39HYuKtpKbOJFjf+N0EgJ+3FvDzlpN4VV6+7/keo7qPZGrfqW3ZLSGE6DDO9s5szc81SZNjlcc4Vnms0bJqhrM3ljBJCEnw+86sEELUptPofPN+NMbpcXLSerLBhMnJqpOctJ6k1F6K3WPncPlhDpcfbrQsg9bgSwI3lDBJCE3AqDMGTDxTqVR+P7bSngYMGMCSJUvqbLv00ksB2LdvH8nJyUB10qK4uJhu3bo1q/yXXnqJZ599lm+//bbeXCU1srOzGTiw+aPSz4bMISLPqIpW5nS6+McrX+M+EoxW0VFqKKD86mwWXvtMvcRDe/B4vLz2+HIMlkjKehziiXn3NXlOZeU+Dh58meKS1QCo1UEkJU6mW7cH0Otj6x1fXmzj42e34rR72Jb8DcUX7WHpdUsJCwpr9f50NPLMvRCiNdW+M1uTJDl9tElDS/I2JFgTzGujX+OypMv8Ol7imRCiNTk8DgqrCutN/lqTMCmoKsDsMDddEBCiDeHda9/1+7EZWWUGsrKyGDhwIIWFhURGRvq233jjjRw4cIC///3vhIeH89hjj3Ho0CEyMzPR6RpfBrm2F198kfnz5/Phhx/6kiwAYWFhhIX9cn0watQorrrqKubPn99q/ZI5RIQ4x4KCdMx87AY+/XAtxzbaibIloPoOfmObw5s3v4ZO7V/gaCtffrMOgyUSh8bGrXf4N8dJWFgvMjL+gdm8hYOHXsVi2c7RY++Qf3wpyUlT6NbtfoKCqucF8Xq8rPpXDk67h4KwXHK6ref9K96TZIgQQrQBf+7Mujyu6kTJGUaa1NyZNQWZzmHrhRDiF3qN3vfYS2McHkedBElD8azMUYbVbSVSH9loOaK+9PR0Bg8ezCeffMKMGTN829977z0eeughrrvuOtRqNVdccQUrVqyokwxRqVQsXryYadOmNVj2W2+9hdPpZNKkSXW2P/XUUyxYsACA/Px8Nm3aVG+USluThIgQbWTS5FH8kLKbLR8dIdKWgGrjpUy1Tefdyf+HXqNvlzZZq+wcWlVBMGFoBpvpkZDarPMjI4cxaOBSzOZNHDz0GuXlOzlydBHH8j8kKek2uqbcw67vHBQcsuDQ2Pi+53s8MfxxekX1apsOCSGEaJJOoyPZmEyyMbnRY2ruzMaFNj5ZrBBCtDe9Rk/X8K50De/a6DE2t42TVSeJDak/ilmc2fz585k3bx7Tp0/3rfISHh7OokWL6qw+U1teXh5arbbOyI+GjmnKn//8Z6ZNm+Z7NOdcCZxJDYTohIZffhETZl+MS+Mgwh5P+o5x/GrRnVQ4KtqlPYvf/Q/BjjAqDaXcfesNLSpDpVIRFXUpgwf9m4yMRRiN6Xi9No4eXcx3X97Ltv8eAmBD2r8Zkz6aG3q0rB4hhBDnTs2d2fZK2AshRGsxaA2kmlIDav6+jmL8+PHMmDGD/PzGJ/I+3YoVK7j//vvp2bPnWdUdFxfHH//4x7MqoyVkhIgQbax7ny7c8UQYHyzcSIQ9jkt238wt/5zG27e/SmpU8yYjOhvL/vs97IoCIO3aEMJDjGdVnkqlIiZ6FNFRV1Baup4D+//Fgc3XgaJiX8xWPF338sCF96EoSsBMaiWEEEIIIYRo3IMPPtis4x944IFWqfeRRx5plXKaS9JmQpwDMYlG7n5qFN5gFyZHLJf/fCu/fn8eq3M2nJP6N+/8iWP/8QBgzzjGLWPGt1rZ1SNGRmLJfgS3NQaLvpgfu3/KHRHF7N51F1u2judY/oe43VWtVqcQQgghhBBCnC1JiAhxjoTHGJj25BWoQ72YHDFcmncTL3/7Bm999S5tudjT4ePH+d/iw2gULZbEY/x2+u2tXse+LQXs316IFw+re77PgpG/J73rrajVBqqqfmbfvvn8b9Ol/Lz/WaqqDrV6/UIIIYQQQgjRXJIQEeIcMkYFM+WxS9EZIdKWwLCj1/H1gRU8+fpfMVvLWr2+SmsVH72+gWBnGBVhxcyYMwGdtnVXubEUWVn74V4Atqd8w/+7fDxXd59En97Pcdmlm+jZ8w8YDN1wuys4enQxm7dcw/Ydt3D8+Ce43ZWt2hYhhBBCCCGE8JckRIQ4x8JjDNwy7xJ0oSpirMkMzL+Gnd5NvPbsJ3yXtb7V6vF6vbz5+nKMllgc2ipunDWI6PDWXX7M7fLw9f/twuNUOG48QOhQOzP6/7JMl04XTteUuxl+yXdcnPEvYqKvBNRYLDvYs/cxNmy8hJycRzCbN6Mo3lZtmxBCCCGEEEKciUyqKkQ7iIgP4f/NHcKnr2wjvjKVjBNX8mPiGkxvd2FT37e4Z8oNJEcmtbh8m9PO2//8lLDDiXhVHjKmxNA7tXsr9qDa+o/3UXrMik1bye6LV/HuyH82OKO3SqUmOvoKoqOvwOE4yYmCzzlx4lOs1kOcKFjGiYJl6PUJxMddR3z89RiN6TIRqxBCCCGEEKJNyQgRIdpJdFIYNz04CI0eEit60K/gctb0+BDT7u68//T/+Pt/P8DlcTW73O+2b+S1J5aj35UIQMSVDq4aPry1m8/ezSfYs7EABS8be3/MwnHPYNKbmjxPr48ntdsMLhm2kkGDPiGxyy1otUYcjgKOHF3Etu038cPmqzh48BUqKnLadH4VIYQQQgghxPlLRogI0Y7iuoVzw28H8fmfd5Bi6Y1H7eLb3v9k7N77cH2l45mt/6LHyEjGDL2cLsYuZyyroKyQf73zFca93TARj1NrI2VMEJMmXt/q7S7Jr+T7JTmAiu3J3zJrwt30ie7TrDJUKhURpkFEmAbRq9cCSkrWU3DyK4qLv8dmO0ze4bfIO/wWwcHJxMWOJTZ2DCbTAFQqTav3RwghhBBCCHH+kYSIEO2sS3cTE2ZezJdvZJJqTsejcrNmyCJGbb+HhJM9qfw3LPl8C+WJ+XTNiOSqYcMxGsIorCiioKiY4uIyCgvKsP4QismRBoArrZg77r2G2JjWnTMEwGlzs/zNbeBWccS0h0sn9uLqblefVZlqtZ7Y2GuIjb0Gt7uK4uLvKSz6hpKS9djtxzhydBFHji4iKCiGmOgriYkZTWTkpWi1oa3UKyGEEEIIIc5vJSUl9OnTh61bt5KamnrO6nU4HPTs2ZPly5czaNCgc1YvSEJEiICQ3DuK636dwX/f/InupQPwHHKza9ynDD54Ha6DBkJc4YQcDsd5GD7/bw5elQeDO+zU2WGoCSMMsBsqGDIphZGXXtkm7VQUhS8XbcdRqlARZCbomiLuS3+qVevQakNJSJhIQsJEPB4bJaXrKSpaSXHxapzOYo6f+ITjJz5BpQoiMnIYMTFXEhM9CoOha6u2QwghhBBCiPPJwoULmTBhgi8Z8tNPP/HCCy+wceNGiouLSU1N5YEHHuDBBx9sdrnLli1j7969GAwGRowYwZ/+9Cd69eoFgF6vZ968eTz66KN89913rd2tM5KEiBABottF0Yy7P51v/i+LC4uHkLPdxZ6Rq3nxNy9xYPdxtv6wh4qfVegdBt85XrUbt8EOIW4i0/Tce9u1BIcEtVkbf1jxMyezrXhUbo4M28SfRy9s08lPNRoDcbFjiYsdi9froqxsK8Ula049VnOE0tINlJZu4GeexmBIJTp6JNHRVxAZMQyNxtB0BUIIIYQQQghsNhuLFi3i66+/9m3bsWMHsbGxLFmyhJSUFDZt2sT999+PRqNh1qxZfpe9bt06Zs6cyZAhQ3C73TzxxBOMGTOGnJwcQkOrR3xPmTKFRx55hD179tCnT/MexT8bKuU8nLGwvLwck8mExWIhPDy8vZsjRB37t59k5aLdoEBWwjqUEQW8Nvo1QnQheL0KJw6XotVqMUWGoA/VnrPVWHZvO8qaRT+jQkVWr1U8/+tHiAqOOid1n05RFKzWQxSXrKa4eA0Wyw4Uxe3br1YHEWEaSlT0ZURFXkZYWO+AWbWmteOPxDMhRHuReCaE6CxaK/7Y7XZyc3NJS0sjODi4FVvY9pYtW8aMGTMoKio643EzZ85kz549rF69usV1FRUVERcXx7p16xg5cqRv++jRo7n88st55plnWlz26Zp6T2SEiBABpufgeDwuL9+/u4f0givI2rSO+5z38ebVbxIZHElSWvQ5b9PBnBOsXrwXNRr2d9nGvGn3tlsyBKonZA0N7U5oaHe6dZ2O212B2fwDJSXrKSlZh91xnFLzRkrNGwEICoohKvJSoqIuIzJqBMH6hHZruxBCCCGEOH8oioLN5WmXug06jd83BdevX8/gwYObPM5isRAVdXbXARaLBaBeOUOHDmXDhg1nVXZzSUJEiADUe3gXPG4vaz/YR3rBFezbYmCacxp/G/M3uoSdebWZ1nY0t5D/vPUTWm8QR6NzuO/X19Ejssc5bUNTtFojsbFjiI0dc2r0yEFKSjdQWroRs3kLTmcxBSe/oODkFwCEhPQgKmoEUZEjiIy8BK3W2M49EEIIIYQQnZHN5aHvk9+2S905z4wlJMi/S/68vDwSExPPeMwPP/zAJ598wn//+98Wt0lRFObOnctll11Gv3796uxLSkoiLy+vxWW3hCREhAhQF12ehDZIw/fv5tCraChB24K5yzmNv417mwsiLjgnbSg4UcKnf95KkDuEQlMek2ePol9cv6ZPbEfVo0d6EBrag64pd+P1OrBYdlJaupGS0o1UVGRjtR7Aaj3AsWPvoVJpMBrTq5MjUSMwhQ9Eo9G3dzeEEEIIIYQ4Z2w22xkf89m9ezc33HADTz75JNdcc02L65k1axa7du1i48aN9fYZDAasVmuLy24JSYgIEcB6DUsgKFjDin9kk2buj+7HYO5x38drY15hQNyANq27uMTMkpfXY3CYKAst4KbZQ+ifENjJmoQX8QAAIABJREFUkIao1XoiIy8hMvISunefh8tVhtm8hVLz/zCbN2G15lJenkl5eSZ5h99CrdYTYRpCZNQIoiKHYzRehEqlae9uCCGEEEKIDsig05DzzNh2q9tfMTExmM3mBvfl5ORw5ZVXMn36dP7whz+0uD2zZ8/myy+/ZP369SQnJ9fbX1paSmxsbIvLbwlJiAgR4NIyYpkw+2L++9ZPJJdfyMjMO3jAMZN7h0zj3n73olG3/sV6XsExPn7tf4RVxVKlL+Pamf24ODm91etpDzpdBHFxY4mLq/5istuPU1q6CbN5E6XmTTidRb75Rw4CWq2JyMhhREaOICryUkJC0gJmglYhhBBCCBHYVCqV34+ttKcBAwawZMmSett3797NlVdeyV133cVzzz3XorIVRWH27NksX76ctWvXkpaW1uBx2dnZDBjQtjd9Txf474wQguRekdz40EC++ksm8ZWp3LjrIT6xvcem45t44fIXSAhtvUlCv/hhJfuWVhHmiMWhtTL61z0YeEH/Vis/0AQHJ5KYOInExEkoikKV9QDm0urkiNm8GbfbQlHRSoqKVgKg1yecmqD1UiIjR6DXn9ssthBCCCGEEK1t7NixPPbYY5jNZiIjI4HqZMjo0aMZM2YMc+fOpaCgAACNRtOskRwzZ87kww8/5IsvvsBoNPrKMZlMGAwG33EbNmzgj3/8Yyv2qmnqc1qbEKLF4lPDuXneIMKjgwl3RHND9m9RdkQz6YtJrMxbedblV7mqePa9v5L3npdQhwlraBlj5/RiSO/Omww5nUqlIiy0Jykpd5HR//8YefkOBg/6jO4XPExkxCWoVEE4HAWcKPiM3Tlz2fi/S9i8ZRwnC79p76YLIYQQQgjRYunp6QwePJhPPvnEt+3f//43RUVFfPDBB3Tp0sX3GjJkiO+YvLw8VCoVa9eubbTst99+G4vFwqhRo+qU8/HHH/uO+eGHH7BYLEyaNKlN+teYNk2ImM1mpk6dislkwmQyMXXqVMrKys54jsPhYPbs2cTExBAaGsrEiRM5duxYnWMefPBBBg0ahF6v5+KLL27LLggRUKISQ7nlD0PpOSQeNRqGHr2OK3beyVMrn+V3639HTklOi8rdeWInT770JpGbLkKrBKF0reA3z1zLRT16tnIPOha1WovJdDGpqb9h4MAPuGLkj1x88bt063o/RmM/QEVV1X5UklsWQgghhBAd3Pz583n99dfxer0ALFiwAEVR6r1qrwSTl5dHREQEGRkZjZbbUBmKojBt2jTfMa+++iqPPPJInREj50KbPjIzefJkjh07xooVKwC4//77mTp1Kl999VWj58yZM4evvvqKpUuXEh0dzcMPP8z111/Pjh070Giq50pQFIV77rmHLVu2sGvXrrbsghABR2/QMubei+jWL5p1H+4jsaIHv/rpd2yyLOf2g5NJj+/Hbb1vY0y3MQRpghotp9ReyneHv2Ptzs1E/HghF5RXrzueNFLPDbeNRqWWeTJOp9EYiI66jOioywBwucyUmjcTGTm8nVsmhBBCCCHE2Rk/fjz79+8nPz+flJQUv85ZsWIFjz/+uO8xm5ZwOBxkZGTw0EMPtbiMllIpiqK0RcF79uyhb9++bN68mWHDhgGwefNmhg8fzt69e+nVq1e9cywWC7Gxsbz//vvceuutABw/fpyUlBS+/vprxo6tOzvvggUL+Pzzz8nMzGxW28rLyzGZTFgsFsLDw1vYQyHan6XIyspFORTmlQNg01ayL3YLe+J/QBPpYWzqWKKCowjRhhCqCyVUF4rVbWVV7neczLbS98RldKmoXsLXq3Uz+s5e9BvarT271Om1dvyReCaEaC8Sz4QQnUVrxR+73U5ubi5paWlnXMJWnDtNvSdtNkLkhx9+wGQy+ZIhAJdccgkmk4lNmzY1mBDZsWMHLpeLMWPG+LYlJibSr18/Nm3aVC8hIsT5zhQbws2PDCRz1RGy1uZDGVx84iouPnEV+eH72ZX7Ex51bp1zjI4oehdeQz+XqXqDWiElw8RlE/sQ1SW0HXohhBBCCCGEEOdemyVECgoKiIuLq7c9Li7ON6tsQ+cEBQXVG24THx/f6Dn+cDgcOBwO3+/l5eUtLkuIQKPRqBk0LpUB13Tl8O5Scjbkczi7hKTyniSVNz4HiN6oIWNUV/pelkioSX8OWyzOhsQzIURnIfFMCCFEe2t2QmTBggU8/fTTZzxm27ZtQPWKDadTFKXB7WfSknNqW7hwYZNtFqKjU2vUpPWPIa1/DBWldvZsOkHRkYp6x2m0aroPjOWCi2PRaGUy0I5G4pkQorOQeCaEEKK9NTshMmvWLG677bYzHpOamsquXbs4efJkvX1FRUXEx8c3eF5CQgJOp7PO2scAhYWFjBgxorlN9XnssceYO3eu7/fy8nK/J4kRoiMyRgUz9Pq09m6GaAMSz4QQnYXEMyGEEO2t2QmRmJgYYmJimjxu+PDhWCwWtm7dytChQwHYsmULFoul0eTGoEGD0Ol0rFq1iltuuQWAEydOkJ2dzYsvvtjcpvro9Xr0enkkQAjR8Uk8E0J0FhLPhBBCtLc2Gy/fp08fxo0bx/Tp09m8eTObN29m+vTpXH/99b4JVfPz8+nduzdbt24FwGQyce+99/Lwww/z/fffs3PnTu644w7S09O5+uqrfWUfOHCAzMxMCgoKsNlsZGZmkpmZidPpbKvuCCGEEEIIIYQQohNps0lVAT744AN++9vf+laNmThxIm+88YZvv8vlYt++fVitVt+21157Da1Wyy233ILNZuOqq67inXfeQaPR+I657777WLdune/3AQMGAJCbm0tqampbdkkIIYQQQgghhBCdQJsmRKKioliyZEmj+1NTU1EUpc624OBg/vrXv/LXv/610fPWrl3bWk0UQgghhBBCCCHOeyUlJfTp04etW7ee04EGDoeDnj17snz5cgYNGnTO6oU2fGRGCCGEEEIIIYQQHcPChQuZMGFCg8mQkpISkpOTUalUlJWVNbvcIUOGYDQaiYuL48Ybb2Tfvn2+/Xq9nnnz5vHoo4+ebReaTRIiQgghhBBCCCHEecxms7Fo0SLuu+++Bvffe++99O/fv0Vlr1u3jpkzZ7J582ZWrVqF2+1mzJgxVFVV+Y6ZMmUKGzZsYM+ePS2qo6Xa9JGZQFXzmE55eXk7t0QIcb6piTunPy7YUhLPhBDtReKZEKKzaO141hF98803aLVahg8fXm/f22+/TVlZGU8++STffPNNs8tesWJFnd8XL15MXFwcO3bsYOTIkQBER0czYsQIPvroI5555pmWdaIFzsuESEVFBYCsdS+EaDcVFRWYTKZWKQckngkh2o/EMyFEZ9Fa8awORQGXtenj2oIuBFQqvw5dv349gwcPrrc9JyeHZ555hi1btnDo0KFWaZbFYgGq5xytbejQoWzYsKFV6vDXeZkQSUxM5OjRoxiNRlR+/gMpLy8nJSWFo0ePEh4e3sYtbHudrT/Q+fok/Ql8LemToihUVFSQmJjYKm2QeCb96Qg6W586W3+gY8YzeR8CX2frD3S+PnW2/kBgxLM6XFZ4vg3K9cfjxyEo1K9D8/Ly6vXf4XBw++2389JLL9G1a9dWSYgoisLcuXO57LLL6NevX519SUlJ5OXlnXUdzXFeJkTUajXJycktOjc8PLzTBAvofP2Bztcn6U/ga26fWvPOg8SzX0h/Al9n61Nn6w90zHgm70Pg62z9gc7Xp87WH2jfeNYR2Ww2goOD62x77LHH6NOnD3fccUer1TNr1ix27drFxo0b6+0zGAxYred2NM15mRARQgghhBBCCCHanC6keqRGe9Xtp5iYGMxmc51tq1evJisri08//RT4ZY6VmJgYnnjiCZ5++ulmNWf27Nl8+eWXrF+/vsEEeGlpKbGxsc0q82xJQkQIIYQQQgghhGgLKpXfj620pwEDBrBkyZI62z777DNsNpvv923btnHPPfewYcMGunfv7nfZiqIwe/Zsli9fztq1a0lLS2vwuOzsbAYMGNCyDrSQZsGCBQvOaY0dmEajYdSoUWi1nSOP1Nn6A52vT9KfwNdR+9RR290Y6U/g62x96mz9gY7Zp47Y5qZ0tj51tv5A5+tTZ+sPtF+f3G43ZWVlREZGdri/T6PRyLPPPsvs2bMxGAxA9aSncXFxvpfFYuHdd9/l5ZdfJjo62u+yZ86cyQcffMCnn35KYmIilZWVVFZWotFo0Ol0vuPmzp3L9OnTycjIaLV+NfWeqJTzeW0hIYQQQgghhBCiFdjtdnJzc0lLS6s3H0dHMHz4cKZNm8aMGTMa3L927VpGjx6N2WwmIiICqJ6MNS0tjTVr1jBq1KgGz2tsouzFixczbdo0AH744QfGjx/P8ePHfQmZ1tDUe9Kx0lZCCCGEEEIIIYRodfPnz2fevHlMnz4dtVpdb/+oUaM4fTxFXl4eERERZxzV4c8YjFdffZVHHnmkVZMh/pCEiBBCCCGEEEIIcZ4bP348+/fvJz8/n5SUFL/OWbFiBY8//jiRkZEtrtfhcJCRkcFDDz3U4jJaSh6ZEUIIIYQQQgghzlJHf2SmM2rqPak/DkYIIYQQQgghhBCik5OEiB/eeustX0Zp0KBBbNiwob2b5Lf169czYcIEEhMTUalUfP7553X2K4rCggULSExMxGAwMGrUKHbv3t1OrW3awoULGTJkCEajkbi4OG688Ub27dtX5xiHw8Hs2bOJiYkhNDSUiRMncuzYsXZq8Zm9/fbb9O/fn/DwcMLDwxk+fDjffPONb39H6ktDFi5ciEqlYs6cOb5tHa1PCxYsQKVS1XklJCT49ne0z5DEs8Ah8Sxw+9IQiWeBR+JZ4JB4Frh9aYjEMyF+IQmRJnz88cfMmTOHJ554gp07d3L55Zdz7bXXcuTIkfZuml+qqqrIyMjgjTfeaHD/iy++yKuvvsobb7zBtm3bSEhI4JprrqGiouIct9Q/69atY+bMmWzevJlVq1bhdrsZM2YMVVVVvmPmzJnD8uXLWbp0KRs3bqSyspLrr78ej8fTji1vWHJyMi+88ALbt29n+/btXHnlldxwww2+gN2R+nK6bdu28fe//53+/fvX2d4R+3TRRRdx4sQJ3ysrK8u3ryN9hiSeBRaJZ4Hbl9NJPAu8z5DEs8Ai8Sxw+3I6iWeB+RkS7UgRZzR06FDlgQceqLOtd+/eyu9///t2alHLAcry5ct9v3u9XiUhIUF54YUXfNvsdrtiMpmUv/3tb+3RxGYrLCxUAGXdunWKoihKWVmZotPplKVLl/qOyc/PV9RqtbJixYr2amazREZGKv/85z87dF8qKiqUnj17KqtWrVKuuOIK5cEHH1QUpWO+P0899ZSSkZHR4L6O9hmSeBbYJJ4FJolngfkZkngW2CSeBSaJZ23/GbLZbEpOTo5is9natB7hv6beExkhcgZOp5MdO3YwZsyYOtvHjBnDpk2b2qlVrSc3N5eCgoI6/dPr9VxxxRUdpn8WiwWAqKgoAHbs2IHL5arTp8TERPr16xfwffJ4PCxdupSqqiqGDx/eofsyc+ZMrrvuOq6++uo62ztqn/bv309iYiJpaWncdtttHDp0COhYnyGJZ4FP4llgkngWeJ8hiWeBT+JZYJJ41nE+Q+LckWV3z6C4uBiPx0N8fHyd7fHx8RQUFLRTq1pPTR8a6t/hw4fbo0nNoigKc+fO5bLLLqNfv35AdZ+CgoLqLfsUyO9ZVlYWw4cPx263ExYWxvLly+nbty+ZmZkdri8AS5cuZceOHWzfvr3evo74/gwbNoz33nuPCy+8kJMnT/Lss88yYsQIdu/e3aE+QxLPApvEs8Ak8SwwP0MSzwKbxLPAJPGs43yGxLklCRE/qFSqOr8rilJvW0fWUfs3a9Ysdu3axcaNG5s8NpD71KtXLzIzMykrK+Ozzz7jrrvuYt26dY0eH8h9OXr0KA8++CArV65s1lJjgdyna6+91vdzeno6w4cPp3v37rz77rtccsklQMf6DHWktrZER+2fxLPAI/GsWiD3pyO1tSU6av8kngUeiWfVArk/ov3IIzNnEBMTg0ajqZcZLSwsrJdx7IhqZmLuiP2bPXs2X375JWvWrCE5Odm3PSEhAafTidlsrnN8IPcpKCiIHj16MHjwYBYuXEhGRgavv/56h+zLjh07KCwsZNCgQWi1WrRaLevWreMvf/kLWq2W+Pj4Dten04WGhpKens7+/fs71GdI4lngkngWmH2ReFYtEPsj8SxwSTwLzL5IPKvWkfrTXkpKSoiLiyMvL6+9m1JPYWEhsbGx5Ofnt2q5khA5g6CgIAYNGsSqVavqbF+1ahUjRoxop1a1nrS0NBISEur0z+l0sm7duoDtn6IozJo1i2XLlrF69WrS0tLq7B80aBA6na5On06cOEF2dnbA9ul0iqLgcDg6ZF+uuuoqsrKyyMzM9L0GDx7MlClTfD93tD6dzuFwsGfPHrp06dKhPkMSzwKPxLPA7ovEs8D9DEk8CzwSzwK7LxLPAv8zFCgWLlzIhAkTSE1N9W178MEHGTRoEHq9nosvvrjB87KysrjiiiswGAwkJSXxzDPPoChKs+pWmlgqOS4ujqlTp/LUU0+1qG9nqlicwdKlSxWdTqcsWrRIycnJUebMmaOEhoYqeXl57d00v1RUVCg7d+5Udu7cqQDKq6++quzcuVM5fPiwoiiK8sILLygmk0lZtmyZkpWVpdx+++1Kly5dlPLy8nZuecN+/etfKyaTSVm7dq1y4sQJ38tqtfqOeeCBB5Tk5GTlu+++U3788UflyiuvVDIyMhS3292OLW/YY489pqxfv17Jzc1Vdu3apTz++OOKWq1WVq5cqShKx+pLY2rPYq4oHa9PDz/8sLJ27Vrl0KFDyubNm5Xrr79eMRqNvhjQkT5DEs8Ci8SzwO1LYySeBQ6JZ4FF4lng9qUxEs/aRkdeZcZqtSoRERHKpk2b6myfPXu28sYbbyhTp05tcGUfi8WixMfHK7fddpuSlZWlfPbZZ4rRaFRefvnlZtX/wgsvKEajUfnss8+UrKws5dZbb633nu3atUsJDg5WSktL/S63qfdEEiJ+ePPNN5Vu3bopQUFBysCBA31LiHUEa9asUYB6r7vuuktRlOplqZ566iklISFB0ev1ysiRI5WsrKz2bfQZNNQXQFm8eLHvGJvNpsyaNUuJiopSDAaDcv311ytHjhxpv0afwT333OP7txUbG6tcddVVvi9bRelYfWnM6V+4Ha1PNcFYp9MpiYmJys0336zs3r3bt7+jfYYkngUOiWeB25fGSDwLLBLPAofEs8DtS2MknrWNjpwQ+eyzz5SYmJhG9ze21PFbb72lmEwmxW63+7YtXLhQSUxMVLxer191N2ep5NTUVGXRokV+lasoTb8nKkVp5lgWIYQQQgghhBBC1GG328nNzSUtLc03ga2iKNjctnZpj0Fr8Hsi2Tlz5rBv3z6++eabBvcvWLCAzz//nMzMzDrb77zzTiwWC1988YVv286dOxk4cCCHDh2q9whdQw4dOkT37t358ccfGTBggG/7DTfcQEREBO+++65v26233kpISAiLFy/2q18NvSe1ySozQgghhBBCCCFEG7C5bQz7cFi71L1l8hZCdCF+HZuXl0diYmKz6ygoKKgz5wj8suRxQUGBXwmR5iyVnJSUxM6dO5vdzsbIpKpCCCGEEEIIIcR5zGazNWtZ5toaWuK4oe0tKef0bQaDAavV2oJWNkxGiAghhBBCCCGEEG3AoDWwZfKWdqvbXzExMfWWXvZHQkJCg0scQ/0RH2cqA6pHinTp0qVOOaeXUVpaSmxsbLPb2RhJiAghhBBCCCGEEG1ApVL5/dhKexowYABLlixp9nnDhw/n8ccfx+l0EhQUBMDKlStJTEys9yhNY2ovlVwzh0jNUsl/+tOf6hybnZ3NqFGjmt3OxsgjM0IIIYQQQgghxHls7Nix7N69u94okQMHDpCZmUlBQQE2m43MzEwyMzNxOp0ATJ48Gb1ez7Rp08jOzmb58uU8//zzzJ071+9HZlQqFXPmzOH5559n+fLlZGdnM23aNEJCQpg8ebLvOKvVyo4dOxgzZkyr9VtGiAghhBBCCCGEEOex9PR0Bg8ezCeffMKMGTN82++77z7WrVvn+71mBEdubi6pqamYTCZWrVrFzJkzGTx4MJGRkcydO5e5c+f6zlm7di2jR4/2ndOQ3/3ud9hsNn7zm99gNpsZNmwYK1euxGg0+o754osv6Nq1K5dffnmr9VuW3RVCCCGEEEIIIc5SU0u8Brqvv/6aefPmkZ2djVrdeg+TvPPOOzz33HPk5OSg0+laXM7QoUOZM2dOnVEjTZFld4UQQgghhBBCCHFG48ePZ//+/eTn55OSktJq5a5YsYLnn3/+rJIhhYWFTJo0idtvv73V2gUyQkQIv6lUKpYvX86NN97Y3k0RQoizIvFMCNFZSDwTgaSjjxDpjJp6T2RSVSFOmTZtGiqVqt5r3Lhx7d00IYRoFolnQojOQuKZEKItySMzQtQybtw4Fi9eXGebXq9vp9YIIUTLSTwTQnQWEs+EEG1FRogIUYterychIaHOKzIyssFjn3nmGeLj48nMzASq18r+3e9+R1JSEqGhoQwbNoy1a9eew9YLIcQvJJ4JIToLiWdCiLYiI0SEaCZFUZgzZw6ff/45GzdupGfPngDcfffd5OXlsXTpUhITE1m+fDnjxo0jKyvLd4wQQgQSiWdCiM5C4pkQoiVkhIgQtfznP/8hLCyszuuPf/yjb7/b7ebOO+9k5cqV/O9///N9kR48eJCPPvqIf//731x++eV0796defPmcdlll9Ub4imEEOeCxDMhRGch8UwI0VZkhIgQtYwePZq33367zraoqCjfzw899BB6vZ7NmzcTExPj2/7jjz+iKAoXXnhhnXMdDgfR0dFt22ghhGiAxDMhRGch8UwI0VYkISJELaGhofTo0aPR/ddccw0fffQR3377LVOmTPFt93q9aDQaduzYgUajqXNOWFhYm7VXCCEaI/FMCNFZSDwTQrQVSYgI0QwTJ05kwoQJTJ48GY1Gw2233QbAgAED8Hg8FBYWcvnll7dzK4UQomkSz4QQnYXEMyFaR0lJCX369GHr1q2kpqa2a1sKCwu56KKLyMzMJCkpqc3qkTlEhKjF4XBQUFBQ51VcXFznmJtuuon333+fu+++m08//RSACy+8kClTpnDnnXeybNkycnNz2bZtG3/605/4+uuv26MrQojznMQzIURnIfFMiHNj4cKFTJgwocFkSElJCcnJyahUKsrKys6qHpfLxaOPPkp6ejqhoaEkJiZy5513cvz4cd8xcXFxTJ06laeeeuqs6mqKjBARopYVK1bQpUuXOtt69erF3r1762ybNGkSXq+XqVOnolarufnmm1m8eDHPPvssDz/8MPn5+URHRzN8+HDGjx9/LrsghBCAxDMhROch8UyItmez2Vi0aFGjycJ7772X/v37k5+ff9Z1Wa1WfvzxR+bPn09GRgZms5k5c+YwceJEtm/f7jvu7rvvZujQobz00kuNLrV9tlSKoihtUrIQQgghhBBCCHGesNvt5ObmkpaWRnBwcHs3p1mWLVvGjBkzKCoqqrfv7bff5uOPP+bJJ5/kqquuwmw2ExER0ar1b9u2jaFDh3L48GG6du3q256Wlsb8+fO55557WlRuU++JjBARQgghhBBCCCHagKIoKDZbu9StMhhQqVR+Hbt+/XoGDx5cb3tOTg7PPPMMW7Zs4dChQ63dRB+LxYJKpaqXaBk6dCgbNmxocUKkKZIQEUIIIYQQQggh2oBis7Fv4KB2qbvXjztQhYT4dWxeXh6JiYl1tjkcDm6//XZeeuklunbt2mYJEbvdzu9//3smT55MeHh4nX1JSUns3LmzTeoFmVRVCCGEEEIIIYQ4r9lstnqPlDz22GP06dOHO+64o8XlfvDBB4SFhfleGzZsqLPf5XJx22234fV6eeutt+qdbzAYsFqtLa6/KTJCRAghhBBCCCGEaAMqg4FeP+5ot7r9FRMTg9lsrrNt9erVZGVl+VZuqpl+NCYmhieeeIKnn366yXInTpzIsGHDfL/XXkLX5XJxyy23kJuby+rVq+uNDgEoLS0lNjbW7340lyREhBBCCCGEEEKINqBSqfx+bKU9DRgwgCVLltTZ9tlnn2GrNf/Jtm3buOeee9iwYQPdu3f3q1yj0YjRaKy3vSYZsn//ftasWUN0dHSD52dnZzNq1Cj/O9JMkhARQgghhBBCCCH+f3v3E9L0/8Bx/PVpatZp/bV/W45OO5gOR2Ag305JQoSwCKkuKV+6fSsGtUlBBnUISi8JQbcgkjoUYYIQ+zkwIlaHpA6CDlEYY7Z12Zw59z2E47dmODdzv36f5wN22Pvz/rzf7w/v24v3HxNra2uTz+dTPB7PXXH7c+gRi8UkSU6ns6xbZhYXF+XxePThwwe9evVKmUxGkUhEkrR9+3bV1NRI+nE9bygU0u3bt0vuazWcIQIAAAAAgIk1NDTI7XZrcHBwTe+Fw2EZhqFAIFD0OzMzM3r58qVmZmbU1NSkvXv35n5jY2O5ei9evJDdbldra+uaxrQWBCIAAAAAAJjc9evX1d/fr6WlpRWfHzt2TNlsNm91SDgcltVqVWNjY9H91NfX/7iOeIXff2+PuX//vm7cuFHy9xSDLTMAAAAAAJhce3u7JiYmNDs7K5vNVtQ7w8PD8vv9uW026yUajcrj8aizs3Nd2/2ZkV0+KhYAAAAAAJRkfn5eU1NTcjgcBVfYojJWmxO2zAAAAAAAANMhEAEAAAAAAKZDIAIAAAAAAEyHQAQAAAAAAJgOgQgAAAAAADAdAhEAAAAAAGA6BCIAAAAAAMB0CEQAAAAAADC5ubk57d69W+FweEP7TafTstvtCoVCG9qvRCACAAAAAIDLAGKgAAAETklEQVTp3blzRydPnlR9fX3Bs7m5OR04cECGYSiRSKy57QcPHsjhcKi2tlbNzc0KBoO5Z5s3b5bX69XVq1fLGX5JCEQAAAAAADCxVCqlR48eqbu7e8XnXV1dOnz4cEltP336VJcuXVJPT48+fvyo1tZWnThxQtPT07k6Z8+eVTAY1JcvX0rqo1QEIgAAAAAAmNjr169VVVWllpaWgmcDAwNKJBLyer0ltX3v3j11dXWpu7tbTqdTfX19stlsGhgYyNXZsWOHjh49qidPnpT8DaWo2tDeAAAAAAAwiWw2q8WFpYr0XVWzSYZhFFV3dHRUbre7oPzz58/q7e3Vu3fvNDk5ueYxLCwsKBQK6dq1a3nlx48f19jYWF7ZkSNH8rbSbAQCEQAAAAAAfoPFhSU9/Oc/Fen77/6/VL3ZUlTdcDisffv25ZWl02l1dnbq7t27stvtJQUisVhMmUxGdXV1eeV1dXWKRCJ5Zfv379/wA13ZMgMAAAAAgImlUinV1tbmlfl8PjmdTp07d67s9n9eqZLNZgvKtmzZomQyWXZfa8EKEQAAAAAAfoOqmk36u/+vivVdrJ07dyoej+eVvXnzRp8+fdKzZ88k/Qgxluv29PTo5s2bRbVrsVgKVoNEo9GCVSNfv37Vrl27ih7zeiAQAQAAAADgNzAMo+htK5Xkcrn0+PHjvLLnz58rlUrl/r9//14XLlxQMBjUoUOHimq3pqZGzc3NGhkZUUdHR658ZGREp06dyqs7Pj4ul8tVxlesHYEIAAAAAAAm1tbWJp/Pp3g8rm3btklSQegRi8UkSU6nU1artei2r1y5ovPnz8vtdqulpUUPHz7U9PS0Ll68mFcvGAzq1q1bZX7J2nCGCAAAAAAAJtbQ0CC3263BwcE1vRcOh2UYhgKBwC/rnDlzRn19fert7VVTU5NGR0c1NDSkgwcP5uq8fftW3759k8fjKfUTSmJklzcCAQAAAACAkszPz2tqakoOh6PggNI/wdDQkLxer8bHx7VpU3FrJwKBgDo6OjQ5OZlbWVKK06dPy+Vyye/3l9zGSlabE7bMAAAAAABgcu3t7ZqYmNDs7KxsNltR7wwPD8vv95cVhqTTaTU2Nury5cslt1EqVogAAAAAAFCmP32FyP+j1eaEM0QAAAAAAIDpEIgAAAAAAADTIRABAAAAAGCdcCrF/47V5oJABAAAAACAMlVXV0uSkslkhUeCZctzsTw3P+OWGQAAAAAAymSxWGS1WhWNRiVJW7dulWEYFR6VOWWzWSWTSUWjUVmtVlkslhXrccsMAAAAAADrIJvNKhKJKJFIVHookGS1WrVnz55fBlMEIgAAAAAArKNMJqPv379XehimVl1d/cuVIcsIRAAAAAAAgOlwqCoAAAAAADAdAhEAAAAAAGA6BCIAAAAAAMB0CEQAAAAAAIDpEIgAAAAAAADTIRABAAAAAACmQyACAAAAAABM518iUg9n531prwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1100.88x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalize and plot results - BLM values vs. polarisation geometry\n",
    "ep.util.matEleSelector(BLM, thres = 0.1, dims='Eke').real.squeeze().plot.line(x='Eke', col='Euler');\n",
    "ep.util.matEleSelector(mTermST, thres = 0.1, dims='Eke').real.squeeze().plot.line(x='Eke', col='Labels');\n",
    "\n",
    "# Replot differences as before - looks like E-dependence, so likely handling of SF (scale factor) is the issue here.\n",
    "ep.util.matEleSelector(testSub, thres = 1e-4, dims='Eke').real.squeeze().plot.line(x='Eke', col='Labels');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NO2\n",
    "Non-linear, 3 symmetry groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** ePSproc readMatEle(): scanning files for DumpIdy segments.\n",
      "\n",
      "*** Scanning file(s)\n",
      "['D:\\\\code\\\\github\\\\ePSproc\\\\data\\\\photoionization\\\\no2_demo_ePS.out']\n",
      "\n",
      "*** Reading ePS output file:  D:\\code\\github\\ePSproc\\data\\photoionization\\no2_demo_ePS.out\n",
      "Expecting 1 energy points.\n",
      "Expecting 3 symmetries.\n",
      "Scanning CrossSection segments.\n",
      "Expecting 3 DumpIdy segments.\n",
      "Found 3 dumpIdy segments (sets of matrix elements).\n",
      "\n",
      "Processing segments to Xarrays...\n",
      "Processed 3 sets of DumpIdy file segments, (0 blank)\n",
      "*** ePSproc readMatEle(): scanning files for CrossSection segments.\n",
      "\n",
      "*** Scanning file(s)\n",
      "['D:\\\\code\\\\github\\\\ePSproc\\\\data\\\\photoionization\\\\no2_demo_ePS.out']\n",
      "\n",
      "*** Reading ePS output file:  D:\\code\\github\\ePSproc\\data\\photoionization\\no2_demo_ePS.out\n",
      "Expecting 1 energy points.\n",
      "Expecting 3 symmetries.\n",
      "Scanning CrossSection segments.\n",
      "Expecting 4 CrossSection segments.\n",
      "Found 4 CrossSection segments (sets of results).\n",
      "Processed 4 sets of CrossSection file segments, (0 blank)\n"
     ]
    }
   ],
   "source": [
    "# Load data from modPath\\data\n",
    "dataPath = os.path.join(modPath, 'data', 'photoionization')\n",
    "dataFile = os.path.join(dataPath, 'no2_demo_ePS.out')  # Set for sample N2 data for testing\n",
    "\n",
    "# Scan data file\n",
    "dataSet = ep.readMatEle(fileIn = dataFile)\n",
    "dataXS = ep.readMatEle(fileIn = dataFile, recordType = 'CrossSection')  # XS info currently not set in NO2 sample file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating MFBLMs for 17956 pairs... Eke = 0.81 eV, eAngs = (<xarray.DataArray ()>\n",
      "array(quaternion(1, -0, 0, 0), dtype=quaternion)\n",
      "Coordinates:\n",
      "    Euler    object (0.0, 0.0, 0.0)\n",
      "    Labels   <U18 'z'\n",
      "Attributes:\n",
      "    dataType:  Euler)\n",
      "Calculating MFBLMs for 17956 pairs... Eke = 0.81 eV, eAngs = (<xarray.DataArray ()>\n",
      "array(quaternion(0.707106781186548, -0, 0.707106781186548, 0),\n",
      "      dtype=quaternion)\n",
      "Coordinates:\n",
      "    Euler    object (0.0, 1.5707963267948966, 0.0)\n",
      "    Labels   <U18 'x'\n",
      "Attributes:\n",
      "    dataType:  Euler)\n",
      "Calculating MFBLMs for 17956 pairs... Eke = 0.81 eV, eAngs = (<xarray.DataArray ()>\n",
      "array(quaternion(0.5, -0.5, 0.5, 0.5), dtype=quaternion)\n",
      "Coordinates:\n",
      "    Euler    object (1.5707963267948966, 1.5707963267948966, 0.0)\n",
      "    Labels   <U18 'y'\n",
      "Attributes:\n",
      "    dataType:  Euler)\n",
      "Elapsed time = 314.4399154186249 seconds, for 1 energy points, 3 polarisations, threshold=0.0001.\n"
     ]
    }
   ],
   "source": [
    "# Set standard pol geoms (== (z,x,y) as used above)\n",
    "eAngs = ep.setPolGeoms()\n",
    "\n",
    "# Calculate for each pol geom\n",
    "start = time.time()\n",
    "BLM = ep.mfblmEuler(dataSet[0], selDims=selDims, eAngs = eAngs, thres = thres, SFflag = True, verbose = 0)  # Run for all Eke.  Note this fails if selDims = None, to fix!\n",
    "end = time.time()\n",
    "print('Elapsed time = {0} seconds, for {1} energy points, {2} polarisations, threshold={3}.'.format((end-start), BLM.Eke.size, eAngs.size, thres))\n",
    "\n",
    "# Elapsed time = 314.4399154186249 seconds, for 1 energy points, 3 polarisations, threshold=0.0001.\n",
    "# NOTE much longer than N2 case (~75s) due to number of matrix elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting data no2_demo_ePS.out, pType=r, thres=0.01, with Seaborn\n",
      "Plotting data no2_demo_ePS.out, pType=r, thres=0.01, with Seaborn\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Full results (before summation)\n",
    "xDim = 'Labels'\n",
    "daPlot, daPlotpd, legendList, gFig = ep.lmPlot(BLM.swap_dims({'Euler':'Labels'}), xDim=xDim, sumDims=None, pType = 'r')   # Plot values scaled by SF\n",
    "daPlot, daPlotpd, legendList, gFig = ep.lmPlot(BLM.swap_dims({'Euler':'Labels'}), xDim=xDim, sumDims=None, pType = 'r', SFflag=False)   # Plot values with no scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time = 7.997419357299805 seconds, for 1 energy points, 3 polarizations, threshold=0.0001.\n"
     ]
    }
   ],
   "source": [
    "symSum = True  # Sum over symmetry groups, or keep separate?\n",
    "\n",
    "start = time.time()\n",
    "mTermST, mTermS, mTermTest = ep.geomFunc.mfblmXprod(dataSet[0], QNs = None, thres = thres, selDims = {'it':1, 'Type':'L'}, thresDims=None, RX=RX, symSum=symSum, SFflag=True, phaseConvention=phaseConvention)\n",
    "end = time.time()\n",
    "print('Elapsed time = {0} seconds, for {1} energy points, {2} polarizations, threshold={3}.'.format((end-start), mTermST.Eke.size, RX.size, thres))\n",
    "\n",
    "# Elapsed time = 7.997419357299805 seconds, for 1 energy points, 3 polarizations, threshold=0.0001.\n",
    "# Note only ~x2 longer than N2 case (3.5s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting data no2_demo_ePS.out, pType=r, thres=0.01, with Seaborn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Full results (before summation)\n",
    "mTermST.attrs['dataType'] = 'matE'  # Set matE here to allow for correct plotting of sym dims.\n",
    "\n",
    "plotDimsRed = ['Labels','L','M']  # Set plotDims to fix dim ordering in plot\n",
    "if not symSum:\n",
    "    plotDimsRed.extend(['Cont','Targ','Total'])\n",
    "    \n",
    "daPlot, daPlotpd, legendList, gFig = ep.lmPlot(mTermST, xDim=xDim, sumDims=None, pType = 'r', thres = 0.01, fillna = False, SFflag=False)  \n",
    "\n",
    "# daPlot, daPlotpd, legendList, gFig = ep.lmPlot(mTermST, xDim='Eke', sumDims=None, pType = 'r', thres = 0.01, fillna = True)  # If plotDims is not passed use default ordering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confirm subtraction OK\n",
    "BLMrename = BLM.swap_dims({'Euler':'Labels'}).unstack('BLM').rename({'l':'L','m':'M'}).stack({'LM':['L','M']})  # Reformat Xarray\n",
    "testSub = (mTermST - BLMrename).squeeze()\n",
    "testSub.attrs['dataType']='matE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting data (No filename), pType=r, thres=None, with Seaborn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daPlot, daPlotpd, legendList, gFig = ep.lmPlot(testSub, xDim=xDim, sumDims=None, pType = 'r', thres = None, fillna = False, SFflag=False)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min difference: (-4.5962933287541086e-05+1.1262398393111875e-17j), max difference: (4.0768195605419937e-05+2.5447083366932985e-23j), threshold: 0.0001\n"
     ]
    }
   ],
   "source": [
    "print(f\"Min difference: {testSub.min().data}, max difference: {testSub.max().data}, threshold: {thres}\")\n",
    "\n",
    "# For thres = 1e-4, max diff = 4e-5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, differences are < threshold, as they should be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Version info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext version_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "3.7.3 64bit [MSC v.1915 64 bit (AMD64)]"
        },
        {
         "module": "IPython",
         "version": "7.12.0"
        },
        {
         "module": "OS",
         "version": "Windows 10 10.0.18362 SP0"
        },
        {
         "module": "epsproc",
         "version": "1.2.5-dev"
        },
        {
         "module": "xarray",
         "version": "0.15.0"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.7.3 64bit [MSC v.1915 64 bit (AMD64)]</td></tr><tr><td>IPython</td><td>7.12.0</td></tr><tr><td>OS</td><td>Windows 10 10.0.18362 SP0</td></tr><tr><td>epsproc</td><td>1.2.5-dev</td></tr><tr><td>xarray</td><td>0.15.0</td></tr><tr><td colspan='2'>Tue Apr 14 15:25:37 2020 Eastern Daylight Time</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.7.3 64bit [MSC v.1915 64 bit (AMD64)] \\\\ \\hline\n",
       "IPython & 7.12.0 \\\\ \\hline\n",
       "OS & Windows 10 10.0.18362 SP0 \\\\ \\hline\n",
       "epsproc & 1.2.5-dev \\\\ \\hline\n",
       "xarray & 0.15.0 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Tue Apr 14 15:25:37 2020 Eastern Daylight Time} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.7.3 64bit [MSC v.1915 64 bit (AMD64)]\n",
       "IPython 7.12.0\n",
       "OS Windows 10 10.0.18362 SP0\n",
       "epsproc 1.2.5-dev\n",
       "xarray 0.15.0\n",
       "Tue Apr 14 15:25:37 2020 Eastern Daylight Time"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%version_information epsproc, xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tue, 14 Apr 2020 15:29:28 +0000'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.strftime(\"%a, %d %b %Y %H:%M:%S +0000\", time.localtime())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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